Analysis and Figures
Samuel R.P-J. Ross
2022-07-08
This markdown is for building models to test effects of land use and typhoons on acoustic indices and bird species detections. Modeling framework uses Bayesian models to test for effects of typhoons and land use (plus their interaction) on means and variabilities of acoustic indices and bird detections. Also tests land use effect on acoustic index resistance and recovery. Can test for spatial autocorrelation of model residuals and fit spatial error terms where needed.
Acoustic indices
acoustic index mean states
NDSI - negative change after typhoon
# get pre vs post data for comparison
dat_pre<-tidy.stability_AI %>%
filter(Index %in% "NDSI" & response_variable %in% 'Pre_mean')
dat_post<-tidy.stability_AI %>%
filter(Index %in% "NDSI" & response_variable %in% 'Post_mean')
dat_pre<-dat_pre[complete.cases(dat_pre),]
dat_post<-dat_post[complete.cases(dat_post),]
dat_pre$Typhoon = rep("Pre", nrow(dat_pre))
dat_post$Typhoon = rep("Post", nrow(dat_post))
df <- rbind(dat_pre, dat_post)
#names(df)[1] <- names(Landuse_1000)[1]
#df <- left_join(df, Landuse_1000, by = "site_id")
# change levels for model
df$Landuse<-df$Landuse %>% parse_character() %>% parse_factor(levels = c('Forest','Developed'))
df$Typhoon<-df$Typhoon %>% parse_character() %>% parse_factor(levels = c('Pre','Post'))
#df$site_id<-df$site_id %>% parse_character() %>% parse_factor(levels = c(df$site_id[order(df$PC1)]))
mod_nonspatial_beta <-
brm(data = df, family = Beta(),
Stability ~ 1 + Landuse * Typhoon + (1|Site_ID),
iter = 5e4, warmup = 5000, chains = 4, cores = 4, thin = 2,
seed = 666)## Compiling Stan program...## Trying to compile a simple C file## Running /Library/Frameworks/R.framework/Resources/bin/R CMD SHLIB foo.c
## clang -mmacosx-version-min=10.13 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/Rcpp/include/" -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/" -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/unsupported" -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/BH/include" -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/StanHeaders/include/src/" -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/StanHeaders/include/" -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppParallel/include/" -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/rstan/include" -DEIGEN_NO_DEBUG -DBOOST_DISABLE_ASSERTS -DBOOST_PENDING_INTEGER_LOG2_HPP -DSTAN_THREADS -DBOOST_NO_AUTO_PTR -include '/Library/Frameworks/R.framework/Versions/4.2/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp' -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1 -I/usr/local/include -fPIC -Wall -g -O2 -c foo.c -o foo.o
## In file included from <built-in>:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.2/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
## In file included from /Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/Eigen/Dense:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/Eigen/Core:88:
## /Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:628:1: error: unknown type name 'namespace'
## namespace Eigen {
## ^
## /Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:628:16: error: expected ';' after top level declarator
## namespace Eigen {
## ^
## ;
## In file included from <built-in>:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.2/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
## In file included from /Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/Eigen/Dense:1:
## /Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/Eigen/Core:96:10: fatal error: 'complex' file not found
## #include <complex>
## ^~~~~~~~~
## 3 errors generated.
## make: *** [foo.o] Error 1## Start samplingmod_nonspatial_beta <- add_criterion(mod_nonspatial_beta, "loo")## Warning: Found 7 observations with a pareto_k > 0.7 in model
## 'mod_nonspatial_beta'. It is recommended to set 'moment_match = TRUE' in order
## to perform moment matching for problematic observations.#check MCMC traces
mcmc_plot(mod_nonspatial_beta, type = "trace") + theme_cowplot()## No divergences to plot.
#check for agreement of chains
mcmc_plot(mod_nonspatial_beta, type = "dens_overlay") + theme_cowplot()
# plot posterior estimates of fixed effects
mcmc_plot(mod_nonspatial_beta, type = "intervals",prob = 0.68, prob_outer = 0.95, variable = "^b_", regex = TRUE) + theme_cowplot()
## Parameter fits and stats
mod_nonspatial_beta$fit # Rhat values closer to 1 and n_eff values > 1000 are ideal.## Inference for Stan model: 516dc2723972b0b4959c45f1ed5792b7.
## 4 chains, each with iter=50000; warmup=5000; thin=2;
## post-warmup draws per chain=22500, total post-warmup draws=90000.
##
## mean se_mean sd 2.5% 25% 50%
## b_Intercept 1.45 0.00 0.23 0.99 1.30 1.45
## b_LanduseDeveloped 0.37 0.00 0.36 -0.35 0.14 0.37
## b_TyphoonPost -0.52 0.00 0.13 -0.78 -0.61 -0.52
## b_LanduseDeveloped:TyphoonPost 0.06 0.00 0.21 -0.36 -0.08 0.06
## sd_Site_ID__Intercept 0.77 0.00 0.14 0.55 0.67 0.76
## phi 54.10 0.07 15.76 27.75 42.79 52.59
## r_Site_ID[CHATANOP,Intercept] -0.71 0.00 0.29 -1.28 -0.90 -0.71
## r_Site_ID[GENKAOP,Intercept] -1.02 0.00 0.32 -1.65 -1.23 -1.02
## r_Site_ID[GESASHIOP,Intercept] 0.68 0.00 0.33 0.05 0.46 0.67
## r_Site_ID[HEIWAOP,Intercept] -1.87 0.00 0.30 -2.46 -2.07 -1.87
## r_Site_ID[HENTONAOP,Intercept] 0.78 0.00 0.38 0.06 0.52 0.77
## r_Site_ID[KATSUUDAKEFR,Intercept] 0.04 0.00 0.35 -0.63 -0.19 0.03
## r_Site_ID[KEMINFR,Intercept] 0.15 0.00 0.35 -0.53 -0.09 0.14
## r_Site_ID[KURASHIKIOP,Intercept] 0.75 0.00 0.33 0.11 0.53 0.74
## r_Site_ID[MANABIFR,Intercept] 0.29 0.00 0.35 -0.39 0.06 0.28
## r_Site_ID[NAGOFR,Intercept] 0.83 0.00 0.34 0.19 0.60 0.82
## r_Site_ID[NAKAGUSUKUFR,Intercept] 0.47 0.00 0.32 -0.15 0.25 0.46
## r_Site_ID[OISTFR,Intercept] 0.12 0.00 0.35 -0.55 -0.11 0.11
## r_Site_ID[OISTOP,Intercept] -0.83 0.00 0.33 -1.47 -1.04 -0.83
## r_Site_ID[OKUFR,Intercept] -0.15 0.00 0.34 -0.81 -0.37 -0.15
## r_Site_ID[OYAMAOP,Intercept] -0.36 0.00 0.29 -0.94 -0.56 -0.37
## r_Site_ID[SEFAFR,Intercept] 0.73 0.00 0.33 0.10 0.50 0.72
## r_Site_ID[SENBARUOP,Intercept] -0.16 0.00 0.30 -0.74 -0.36 -0.16
## r_Site_ID[SUEYOSHIFR,Intercept] 0.27 0.00 0.31 -0.33 0.06 0.27
## r_Site_ID[TAKEYANBARUF,Intercept] 0.05 0.00 0.35 -0.61 -0.18 0.05
## r_Site_ID[TAMAGUSUKUFR,Intercept] 0.57 0.00 0.33 -0.06 0.35 0.56
## r_Site_ID[TOUNANOP,Intercept] -0.01 0.00 0.30 -0.60 -0.22 -0.02
## r_Site_ID[UEHARAFR,Intercept] -0.45 0.00 0.30 -1.03 -0.65 -0.45
## r_Site_ID[YACHOOP,Intercept] -0.60 0.00 0.29 -1.17 -0.79 -0.60
## r_Site_ID[YONAFR,Intercept] 0.63 0.00 0.37 -0.07 0.38 0.62
## lprior -12.49 0.00 0.46 -13.39 -12.80 -12.49
## lp__ 31.45 0.04 6.34 17.82 27.47 31.90
## 75% 97.5% n_eff Rhat
## b_Intercept 1.60 1.92 31234 1
## b_LanduseDeveloped 0.61 1.09 33341 1
## b_TyphoonPost -0.44 -0.26 73820 1
## b_LanduseDeveloped:TyphoonPost 0.20 0.48 73790 1
## sd_Site_ID__Intercept 0.85 1.10 36781 1
## phi 63.71 89.29 54172 1
## r_Site_ID[CHATANOP,Intercept] -0.52 -0.14 40136 1
## r_Site_ID[GENKAOP,Intercept] -0.80 -0.38 39450 1
## r_Site_ID[GESASHIOP,Intercept] 0.89 1.35 45359 1
## r_Site_ID[HEIWAOP,Intercept] -1.67 -1.28 41416 1
## r_Site_ID[HENTONAOP,Intercept] 1.02 1.56 48928 1
## r_Site_ID[KATSUUDAKEFR,Intercept] 0.26 0.74 42361 1
## r_Site_ID[KEMINFR,Intercept] 0.38 0.86 44371 1
## r_Site_ID[KURASHIKIOP,Intercept] 0.97 1.43 45754 1
## r_Site_ID[MANABIFR,Intercept] 0.52 1.01 44389 1
## r_Site_ID[NAGOFR,Intercept] 1.05 1.52 46473 1
## r_Site_ID[NAKAGUSUKUFR,Intercept] 0.68 1.12 43841 1
## r_Site_ID[OISTFR,Intercept] 0.35 0.82 43893 1
## r_Site_ID[OISTOP,Intercept] -0.61 -0.18 41019 1
## r_Site_ID[OKUFR,Intercept] 0.07 0.53 43417 1
## r_Site_ID[OYAMAOP,Intercept] -0.17 0.22 40450 1
## r_Site_ID[SEFAFR,Intercept] 0.95 1.41 46642 1
## r_Site_ID[SENBARUOP,Intercept] 0.04 0.44 40273 1
## r_Site_ID[SUEYOSHIFR,Intercept] 0.47 0.90 42185 1
## r_Site_ID[TAKEYANBARUF,Intercept] 0.28 0.76 43710 1
## r_Site_ID[TAMAGUSUKUFR,Intercept] 0.78 1.24 45281 1
## r_Site_ID[TOUNANOP,Intercept] 0.18 0.59 42028 1
## r_Site_ID[UEHARAFR,Intercept] -0.26 0.14 40749 1
## r_Site_ID[YACHOOP,Intercept] -0.41 -0.03 39848 1
## r_Site_ID[YONAFR,Intercept] 0.87 1.40 46216 1
## lprior -12.18 -11.59 50678 1
## lp__ 35.91 42.57 28506 1
##
## Samples were drawn using NUTS(diag_e) at Mon Nov 14 13:22:32 2022.
## For each parameter, n_eff is a crude measure of effective sample size,
## and Rhat is the potential scale reduction factor on split chains (at
## convergence, Rhat=1).bayes_R2(mod_nonspatial_beta) # pseudo r-squared of model## Estimate Est.Error Q2.5 Q97.5
## R2 0.8644529 0.04139113 0.7596527 0.9174749# simulate data from 500 random draws of posterior and compare it to observed data
# the black line should run through the center of the blue lines
pp_check(mod_nonspatial_beta, ndraws = 500) + theme_cowplot()
## Spatial Autocorrelation test
bres <- residuals(mod_nonspatial_beta)[,"Estimate"]
d_mat = as.matrix(dist(df[,c("Lat", "Long")], diag=T, upper=T)) # make distance matrix
d_mat_inv <- 1/d_mat
d_mat_inv[which(d_mat_inv == Inf)] <- 0
Moran.I(bres, d_mat_inv) # non-significant = no spatial autocorrelation## $observed
## [1] -0.007368338
##
## $expected
## [1] -0.0212766
##
## $sd
## [1] 0.0411747
##
## $p.value
## [1] 0.7355241summary(mod_nonspatial_beta)## Family: beta
## Links: mu = logit; phi = identity
## Formula: Stability ~ 1 + Landuse * Typhoon + (1 | Site_ID)
## Data: df (Number of observations: 48)
## Draws: 4 chains, each with iter = 50000; warmup = 5000; thin = 2;
## total post-warmup draws = 90000
##
## Group-Level Effects:
## ~Site_ID (Number of levels: 24)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.77 0.14 0.55 1.10 1.00 36580 56347
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## Intercept 1.45 0.23 0.99 1.92 1.00 31176
## LanduseDeveloped 0.37 0.36 -0.35 1.09 1.00 33282
## TyphoonPost -0.52 0.13 -0.78 -0.26 1.00 73958
## LanduseDeveloped:TyphoonPost 0.06 0.21 -0.36 0.48 1.00 73892
## Tail_ESS
## Intercept 51479
## LanduseDeveloped 53671
## TyphoonPost 77526
## LanduseDeveloped:TyphoonPost 78617
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## phi 54.10 15.76 27.75 89.29 1.00 52795 68793
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).plot results:
mut_mod<-mod_nonspatial_beta %>%
spread_draws(b_TyphoonPost, r_Site_ID[Site_ID,]) %>%
mutate(site_mean = b_TyphoonPost + r_Site_ID) # get posterior distribution data for plotting## Warning: `gather_()` was deprecated in tidyr 1.2.0.
## ℹ Please use `gather()` instead.
## ℹ The deprecated feature was likely used in the tidybayes package.
## Please report the issue at <]8;;https://github.com/mjskay/tidybayes/issues/newhttps://github.com/mjskay/tidybayes/issues/new]8;;>.# determine whether credible intervals span zero, and store as binary (to show in plot):
mut_mod$sig<-0
for (i in 1:length(unique(mut_mod$Site_ID))) {
temp<-mut_mod$site_mean[mut_mod$Site_ID %in% mut_mod$Site_ID[i]] %>%
quantile(c(0.05,0.95)) %>% # get 95% credible intervals
sign() %>% # get sign of those intervals
duplicated() # are the signs identical (no = zero-spanning)
if(temp[2] %in% TRUE){ # if signs are identical (non-zero-spanning)
mut_mod$sig[mut_mod$Site_ID %in% mut_mod$Site_ID[i]]<-1 # add significance indicator
}
}
rm(temp)
mut_mod$sig<-mut_mod$sig %>% as.character() %>% parse_factor(levels = c('0','1')) # convert to factor
# plot posterior typhoon effect
mut_mod %>%
ggplot(aes(y = Site_ID %>% as.character %>% parse_factor(levels = Site_order),
x = site_mean,
fill = stat(x) > 0,
shape = sig)) +
stat_halfeye(show.legend = F,slab_type = "pdf") +
geom_vline(xintercept = 0,
linetype = "dashed") +
scale_fill_manual(values = c("gray80","skyblue")) +
scale_shape_manual(values = c(21,8)) +
labs(x = "Posterior draws (typhoon effect)",
y = "Site") +
cowplot::theme_minimal_grid(font_size = 14,
colour = "grey92")
NDSI_Bio - no effect of typhoon or land use
# get pre vs post data for comparison
dat_pre<-tidy.stability_AI %>%
filter(Index %in% "NDSI_Bio" & response_variable %in% 'Pre_mean')
dat_post<-tidy.stability_AI %>%
filter(Index %in% "NDSI_Bio" & response_variable %in% 'Post_mean')
dat_pre<-dat_pre[complete.cases(dat_pre),]
dat_post<-dat_post[complete.cases(dat_post),]
dat_pre$Typhoon = rep("Pre", nrow(dat_pre))
dat_post$Typhoon = rep("Post", nrow(dat_post))
df <- rbind(dat_pre, dat_post)
#names(df)[1] <- names(Landuse_1000)[1]
#df <- left_join(df, Landuse_1000, by = "site_id")
# change levels for model
df$Landuse<-df$Landuse %>% parse_character() %>% parse_factor(levels = c('Forest','Developed'))
df$Typhoon<-df$Typhoon %>% parse_character() %>% parse_factor(levels = c('Pre','Post'))
#df$site_id<-df$site_id %>% parse_character() %>% parse_factor(levels = c(df$site_id[order(df$PC1)]))
mod_nonspatial_beta <-
brm(data = df, family = Beta(),
Stability ~ 1 + Landuse * Typhoon + (1|Site_ID),
iter = 5e4, warmup = 5000, chains = 4, cores = 4, thin = 2,
seed = 666)## Compiling Stan program...## Start samplingmod_nonspatial_beta <- add_criterion(mod_nonspatial_beta, "loo")## Warning: Found 2 observations with a pareto_k > 0.7 in model
## 'mod_nonspatial_beta'. It is recommended to set 'moment_match = TRUE' in order
## to perform moment matching for problematic observations.#check MCMC traces
mcmc_plot(mod_nonspatial_beta, type = "trace") + theme_cowplot()## No divergences to plot.
#check for agreement of chains
mcmc_plot(mod_nonspatial_beta, type = "dens_overlay") + theme_cowplot()
# plot posterior estimates of fixed effects
mcmc_plot(mod_nonspatial_beta, type = "intervals",prob = 0.68, prob_outer = 0.95, variable = "^b_", regex = TRUE) + theme_cowplot()
## no effects ##
## Parameter fits and stats
mod_nonspatial_beta$fit # Rhat values closer to 1 and n_eff values > 1000 are ideal.## Inference for Stan model: 516dc2723972b0b4959c45f1ed5792b7.
## 4 chains, each with iter=50000; warmup=5000; thin=2;
## post-warmup draws per chain=22500, total post-warmup draws=90000.
##
## mean se_mean sd 2.5% 25% 50%
## b_Intercept -0.12 0.00 0.14 -0.39 -0.21 -0.12
## b_LanduseDeveloped 0.16 0.00 0.22 -0.26 0.02 0.16
## b_TyphoonPost -0.06 0.00 0.13 -0.31 -0.14 -0.06
## b_LanduseDeveloped:TyphoonPost -0.02 0.00 0.20 -0.41 -0.15 -0.02
## sd_Site_ID__Intercept 0.38 0.00 0.10 0.21 0.32 0.38
## phi 39.28 0.06 11.98 19.32 30.69 38.11
## r_Site_ID[CHATANOP,Intercept] -0.20 0.00 0.22 -0.63 -0.34 -0.19
## r_Site_ID[GENKAOP,Intercept] -0.18 0.00 0.23 -0.64 -0.33 -0.18
## r_Site_ID[GESASHIOP,Intercept] 0.16 0.00 0.22 -0.26 0.02 0.16
## r_Site_ID[HEIWAOP,Intercept] -0.99 0.00 0.30 -1.57 -1.19 -1.00
## r_Site_ID[HENTONAOP,Intercept] 0.20 0.00 0.23 -0.24 0.05 0.20
## r_Site_ID[KATSUUDAKEFR,Intercept] -0.03 0.00 0.22 -0.47 -0.17 -0.02
## r_Site_ID[KEMINFR,Intercept] 0.19 0.00 0.23 -0.26 0.03 0.18
## r_Site_ID[KURASHIKIOP,Intercept] 0.20 0.00 0.22 -0.23 0.05 0.19
## r_Site_ID[MANABIFR,Intercept] -0.09 0.00 0.22 -0.54 -0.24 -0.09
## r_Site_ID[NAGOFR,Intercept] 0.38 0.00 0.23 -0.06 0.22 0.38
## r_Site_ID[NAKAGUSUKUFR,Intercept] 0.48 0.00 0.24 0.02 0.32 0.49
## r_Site_ID[OISTFR,Intercept] 0.00 0.00 0.22 -0.44 -0.15 0.00
## r_Site_ID[OISTOP,Intercept] -0.31 0.00 0.23 -0.79 -0.47 -0.31
## r_Site_ID[OKUFR,Intercept] -0.07 0.00 0.22 -0.52 -0.22 -0.07
## r_Site_ID[OYAMAOP,Intercept] -0.17 0.00 0.22 -0.61 -0.32 -0.17
## r_Site_ID[SEFAFR,Intercept] 0.32 0.00 0.23 -0.11 0.17 0.32
## r_Site_ID[SENBARUOP,Intercept] -0.11 0.00 0.22 -0.55 -0.25 -0.11
## r_Site_ID[SUEYOSHIFR,Intercept] -0.03 0.00 0.22 -0.46 -0.17 -0.03
## r_Site_ID[TAKEYANBARUF,Intercept] 0.25 0.00 0.23 -0.19 0.10 0.25
## r_Site_ID[TAMAGUSUKUFR,Intercept] 0.28 0.00 0.22 -0.15 0.13 0.28
## r_Site_ID[TOUNANOP,Intercept] -0.05 0.00 0.22 -0.48 -0.19 -0.05
## r_Site_ID[UEHARAFR,Intercept] 0.00 0.00 0.22 -0.43 -0.15 0.00
## r_Site_ID[YACHOOP,Intercept] -0.27 0.00 0.22 -0.72 -0.42 -0.27
## r_Site_ID[YONAFR,Intercept] 0.05 0.00 0.22 -0.40 -0.10 0.05
## lprior -11.79 0.00 0.43 -12.62 -12.07 -11.79
## lp__ 11.11 0.04 6.89 -4.04 6.84 11.66
## 75% 97.5% n_eff Rhat
## b_Intercept -0.03 0.16 66365 1
## b_LanduseDeveloped 0.31 0.59 67086 1
## b_TyphoonPost 0.03 0.19 81927 1
## b_LanduseDeveloped:TyphoonPost 0.11 0.37 84023 1
## sd_Site_ID__Intercept 0.44 0.59 37528 1
## phi 46.53 66.10 47388 1
## r_Site_ID[CHATANOP,Intercept] -0.05 0.23 67034 1
## r_Site_ID[GENKAOP,Intercept] -0.03 0.25 66753 1
## r_Site_ID[GESASHIOP,Intercept] 0.30 0.59 69049 1
## r_Site_ID[HEIWAOP,Intercept] -0.80 -0.36 44594 1
## r_Site_ID[HENTONAOP,Intercept] 0.35 0.65 67669 1
## r_Site_ID[KATSUUDAKEFR,Intercept] 0.12 0.41 69143 1
## r_Site_ID[KEMINFR,Intercept] 0.33 0.64 66051 1
## r_Site_ID[KURASHIKIOP,Intercept] 0.34 0.63 68340 1
## r_Site_ID[MANABIFR,Intercept] 0.06 0.34 69368 1
## r_Site_ID[NAGOFR,Intercept] 0.53 0.83 62249 1
## r_Site_ID[NAKAGUSUKUFR,Intercept] 0.64 0.95 56803 1
## r_Site_ID[OISTFR,Intercept] 0.14 0.43 68255 1
## r_Site_ID[OISTOP,Intercept] -0.16 0.13 63085 1
## r_Site_ID[OKUFR,Intercept] 0.07 0.36 68847 1
## r_Site_ID[OYAMAOP,Intercept] -0.03 0.25 68638 1
## r_Site_ID[SEFAFR,Intercept] 0.47 0.77 63576 1
## r_Site_ID[SENBARUOP,Intercept] 0.03 0.31 70511 1
## r_Site_ID[SUEYOSHIFR,Intercept] 0.11 0.39 71330 1
## r_Site_ID[TAKEYANBARUF,Intercept] 0.40 0.71 63150 1
## r_Site_ID[TAMAGUSUKUFR,Intercept] 0.43 0.73 63898 1
## r_Site_ID[TOUNANOP,Intercept] 0.09 0.37 70160 1
## r_Site_ID[UEHARAFR,Intercept] 0.14 0.42 70037 1
## r_Site_ID[YACHOOP,Intercept] -0.12 0.15 67128 1
## r_Site_ID[YONAFR,Intercept] 0.19 0.49 69208 1
## lprior -11.50 -10.92 43801 1
## lp__ 15.97 23.07 27026 1
##
## Samples were drawn using NUTS(diag_e) at Mon Nov 14 13:24:08 2022.
## For each parameter, n_eff is a crude measure of effective sample size,
## and Rhat is the potential scale reduction factor on split chains (at
## convergence, Rhat=1).bayes_R2(mod_nonspatial_beta) # pseudo r-squared of model## Estimate Est.Error Q2.5 Q97.5
## R2 0.5522608 0.1116597 0.270455 0.7092613# simulate data from 500 random draws of posterior and compare it to observed data
# the black line should run through the center of the blue lines
pp_check(mod_nonspatial_beta, ndraws = 500) + theme_cowplot()
## Spatial Autocorrelation test
bres <- residuals(mod_nonspatial_beta)[,"Estimate"]
d_mat = as.matrix(dist(df[,c("Lat", "Long")], diag=T, upper=T)) # make distance matrix
d_mat_inv <- 1/d_mat
d_mat_inv[which(d_mat_inv == Inf)] <- 0
Moran.I(bres, d_mat_inv) # non-significant = no spatial autocorrelation## $observed
## [1] -0.01638461
##
## $expected
## [1] -0.0212766
##
## $sd
## [1] 0.04114045
##
## $p.value
## [1] 0.9053471summary(mod_nonspatial_beta)## Family: beta
## Links: mu = logit; phi = identity
## Formula: Stability ~ 1 + Landuse * Typhoon + (1 | Site_ID)
## Data: df (Number of observations: 48)
## Draws: 4 chains, each with iter = 50000; warmup = 5000; thin = 2;
## total post-warmup draws = 90000
##
## Group-Level Effects:
## ~Site_ID (Number of levels: 24)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.38 0.10 0.21 0.59 1.00 38093 43725
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## Intercept -0.12 0.14 -0.39 0.16 1.00 66450
## LanduseDeveloped 0.16 0.22 -0.26 0.59 1.00 67120
## TyphoonPost -0.06 0.13 -0.31 0.19 1.00 81924
## LanduseDeveloped:TyphoonPost -0.02 0.20 -0.41 0.37 1.00 84017
## Tail_ESS
## Intercept 74207
## LanduseDeveloped 73786
## TyphoonPost 80274
## LanduseDeveloped:TyphoonPost 79873
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## phi 39.28 11.98 19.32 66.10 1.00 44675 50796
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).NDSI_Anthro - positive change after typhoon
# get pre vs post data for comparison
dat_pre<-tidy.stability_AI %>%
filter(Index %in% "NDSI_Anth" & response_variable %in% 'Pre_mean')
dat_post<-tidy.stability_AI %>%
filter(Index %in% "NDSI_Anth" & response_variable %in% 'Post_mean')
dat_pre<-dat_pre[complete.cases(dat_pre),]
dat_post<-dat_post[complete.cases(dat_post),]
dat_pre$Typhoon = rep("Pre", nrow(dat_pre))
dat_post$Typhoon = rep("Post", nrow(dat_post))
df <- rbind(dat_pre, dat_post)
#names(df)[1] <- names(Landuse_1000)[1]
#df <- left_join(df, Landuse_1000, by = "site_id")
# change levels for model
df$Landuse<-df$Landuse %>% parse_character() %>% parse_factor(levels = c('Forest','Developed'))
df$Typhoon<-df$Typhoon %>% parse_character() %>% parse_factor(levels = c('Pre','Post'))
#df$site_id<-df$site_id %>% parse_character() %>% parse_factor(levels = c(df$site_id[order(df$PC1)]))
mod_nonspatial_beta <-
brm(data = df, family = Beta(),
Stability ~ 1 + Landuse * Typhoon + (1|Site_ID),
iter = 5e4, warmup = 5000, chains = 4, cores = 4, thin = 2,
seed = 666)## Compiling Stan program...## Start samplingmod_nonspatial_beta <- add_criterion(mod_nonspatial_beta, "loo")## Warning: Found 10 observations with a pareto_k > 0.7 in model
## 'mod_nonspatial_beta'. It is recommended to set 'moment_match = TRUE' in order
## to perform moment matching for problematic observations.#check MCMC traces
mcmc_plot(mod_nonspatial_beta, type = "trace") + theme_cowplot()## No divergences to plot.
#check for agreement of chains
mcmc_plot(mod_nonspatial_beta, type = "dens_overlay") + theme_cowplot()
# plot posterior estimates of fixed effects
mcmc_plot(mod_nonspatial_beta, type = "intervals",prob = 0.68, prob_outer = 0.95, variable = "^b_", regex = TRUE) + theme_cowplot()
## Parameter fits and stats
mod_nonspatial_beta$fit # Rhat values closer to 1 and n_eff values > 1000 are ideal.## Inference for Stan model: 516dc2723972b0b4959c45f1ed5792b7.
## 4 chains, each with iter=50000; warmup=5000; thin=2;
## post-warmup draws per chain=22500, total post-warmup draws=90000.
##
## mean se_mean sd 2.5% 25% 50%
## b_Intercept -0.51 0.00 0.26 -1.01 -0.67 -0.51
## b_LanduseDeveloped -0.44 0.00 0.40 -1.23 -0.70 -0.44
## b_TyphoonPost 0.68 0.00 0.17 0.34 0.57 0.68
## b_LanduseDeveloped:TyphoonPost -0.12 0.00 0.26 -0.62 -0.29 -0.12
## sd_Site_ID__Intercept 0.84 0.00 0.16 0.57 0.73 0.82
## phi 26.25 0.03 7.78 13.27 20.69 25.46
## r_Site_ID[CHATANOP,Intercept] 0.80 0.00 0.36 0.11 0.57 0.80
## r_Site_ID[GENKAOP,Intercept] 0.99 0.00 0.38 0.25 0.74 0.99
## r_Site_ID[GESASHIOP,Intercept] -0.83 0.00 0.36 -1.56 -1.06 -0.82
## r_Site_ID[HEIWAOP,Intercept] 2.07 0.00 0.43 1.25 1.78 2.06
## r_Site_ID[HENTONAOP,Intercept] -0.77 0.00 0.41 -1.59 -1.03 -0.76
## r_Site_ID[KATSUUDAKEFR,Intercept] -0.07 0.00 0.38 -0.83 -0.32 -0.07
## r_Site_ID[KEMINFR,Intercept] -0.08 0.00 0.39 -0.86 -0.34 -0.08
## r_Site_ID[KURASHIKIOP,Intercept] -0.91 0.00 0.37 -1.67 -1.15 -0.91
## r_Site_ID[MANABIFR,Intercept] -0.28 0.00 0.39 -1.05 -0.53 -0.28
## r_Site_ID[NAGOFR,Intercept] -0.84 0.00 0.37 -1.59 -1.09 -0.84
## r_Site_ID[NAKAGUSUKUFR,Intercept] -0.44 0.00 0.36 -1.16 -0.67 -0.43
## r_Site_ID[OISTFR,Intercept] -0.13 0.00 0.38 -0.90 -0.39 -0.13
## r_Site_ID[OISTOP,Intercept] 0.74 0.00 0.38 -0.01 0.48 0.74
## r_Site_ID[OKUFR,Intercept] 0.09 0.00 0.38 -0.68 -0.16 0.09
## r_Site_ID[OYAMAOP,Intercept] 0.34 0.00 0.35 -0.34 0.11 0.34
## r_Site_ID[SEFAFR,Intercept] -0.76 0.00 0.37 -1.51 -1.00 -0.75
## r_Site_ID[SENBARUOP,Intercept] 0.17 0.00 0.35 -0.52 -0.06 0.17
## r_Site_ID[SUEYOSHIFR,Intercept] -0.34 0.00 0.35 -1.04 -0.57 -0.33
## r_Site_ID[TAKEYANBARUF,Intercept] 0.07 0.00 0.39 -0.70 -0.18 0.07
## r_Site_ID[TAMAGUSUKUFR,Intercept] -0.59 0.00 0.37 -1.34 -0.84 -0.59
## r_Site_ID[TOUNANOP,Intercept] -0.09 0.00 0.35 -0.78 -0.32 -0.09
## r_Site_ID[UEHARAFR,Intercept] 0.86 0.00 0.36 0.15 0.62 0.86
## r_Site_ID[YACHOOP,Intercept] 0.52 0.00 0.35 -0.16 0.29 0.52
## r_Site_ID[YONAFR,Intercept] -0.57 0.00 0.40 -1.38 -0.83 -0.56
## lprior -11.33 0.00 0.38 -12.06 -11.60 -11.34
## lp__ 6.76 0.04 6.58 -7.50 2.67 7.25
## 75% 97.5% n_eff Rhat
## b_Intercept -0.34 0.00 35142 1
## b_LanduseDeveloped -0.18 0.34 37730 1
## b_TyphoonPost 0.79 1.00 75705 1
## b_LanduseDeveloped:TyphoonPost 0.05 0.40 77840 1
## sd_Site_ID__Intercept 0.93 1.21 36870 1
## phi 30.98 43.58 51778 1
## r_Site_ID[CHATANOP,Intercept] 1.04 1.51 46221 1
## r_Site_ID[GENKAOP,Intercept] 1.24 1.76 47218 1
## r_Site_ID[GESASHIOP,Intercept] -0.58 -0.12 48761 1
## r_Site_ID[HEIWAOP,Intercept] 2.34 2.94 50357 1
## r_Site_ID[HENTONAOP,Intercept] -0.49 0.01 49059 1
## r_Site_ID[KATSUUDAKEFR,Intercept] 0.19 0.68 47566 1
## r_Site_ID[KEMINFR,Intercept] 0.18 0.68 47843 1
## r_Site_ID[KURASHIKIOP,Intercept] -0.66 -0.19 48066 1
## r_Site_ID[MANABIFR,Intercept] -0.02 0.47 47519 1
## r_Site_ID[NAGOFR,Intercept] -0.60 -0.12 48531 1
## r_Site_ID[NAKAGUSUKUFR,Intercept] -0.20 0.27 48851 1
## r_Site_ID[OISTFR,Intercept] 0.12 0.61 46565 1
## r_Site_ID[OISTOP,Intercept] 0.99 1.49 46543 1
## r_Site_ID[OKUFR,Intercept] 0.34 0.83 47021 1
## r_Site_ID[OYAMAOP,Intercept] 0.57 1.04 47050 1
## r_Site_ID[SEFAFR,Intercept] -0.51 -0.04 49403 1
## r_Site_ID[SENBARUOP,Intercept] 0.40 0.85 46123 1
## r_Site_ID[SUEYOSHIFR,Intercept] -0.10 0.35 46931 1
## r_Site_ID[TAKEYANBARUF,Intercept] 0.33 0.82 47210 1
## r_Site_ID[TAMAGUSUKUFR,Intercept] -0.35 0.12 48776 1
## r_Site_ID[TOUNANOP,Intercept] 0.15 0.60 46369 1
## r_Site_ID[UEHARAFR,Intercept] 1.10 1.60 48542 1
## r_Site_ID[YACHOOP,Intercept] 0.75 1.21 44575 1
## r_Site_ID[YONAFR,Intercept] -0.30 0.20 48047 1
## lprior -11.08 -10.56 46936 1
## lp__ 11.39 18.14 28032 1
##
## Samples were drawn using NUTS(diag_e) at Mon Nov 14 13:25:36 2022.
## For each parameter, n_eff is a crude measure of effective sample size,
## and Rhat is the potential scale reduction factor on split chains (at
## convergence, Rhat=1).bayes_R2(mod_nonspatial_beta) # pseudo r-squared of model## Estimate Est.Error Q2.5 Q97.5
## R2 0.807011 0.0520729 0.6761066 0.8764128# simulate data from 500 random draws of posterior and compare it to observed data
# the black line should run through the center of the blue lines
pp_check(mod_nonspatial_beta, ndraws = 500) + theme_cowplot()
## Spatial Autocorrelation test
bres <- residuals(mod_nonspatial_beta)[,"Estimate"]
d_mat = as.matrix(dist(df[,c("Lat", "Long")], diag=T, upper=T)) # make distance matrix
d_mat_inv <- 1/d_mat
d_mat_inv[which(d_mat_inv == Inf)] <- 0
Moran.I(bres, d_mat_inv) # non-significant = no spatial autocorrelation## $observed
## [1] -0.006962
##
## $expected
## [1] -0.0212766
##
## $sd
## [1] 0.04120184
##
## $p.value
## [1] 0.7282712Plot typhoon effect:
mut_mod<-mod_nonspatial_beta %>%
spread_draws(b_TyphoonPost, r_Site_ID[Site_ID,]) %>%
mutate(site_mean = b_TyphoonPost + r_Site_ID) # get posterior distribution data for plotting
# determine whether credible intervals span zero, and store as binary (to show in plot):
mut_mod$sig<-0
for (i in 1:length(unique(mut_mod$Site_ID))) {
temp<-mut_mod$site_mean[mut_mod$Site_ID %in% mut_mod$Site_ID[i]] %>%
quantile(c(0.05,0.95)) %>% # get 95% credible intervals
sign() %>% # get sign of those intervals
duplicated() # are the signs identical (no = zero-spanning)
if(temp[2] %in% TRUE){ # if signs are identical (non-zero-spanning)
mut_mod$sig[mut_mod$Site_ID %in% mut_mod$Site_ID[i]]<-1 # add significance indicator
}
}
rm(temp)
mut_mod$sig<-mut_mod$sig %>% as.character() %>% parse_factor(levels = c('0','1')) # convert to factor
# plot posterior typhoon effect
mut_mod %>%
ggplot(aes(y = Site_ID %>% as.character %>% parse_factor(levels = Site_order),
x = site_mean,
fill = stat(x) > 0,
shape = sig)) +
stat_halfeye(show.legend = F,slab_type = "pdf") +
geom_vline(xintercept = 0,
linetype = "dashed") +
scale_fill_manual(values = c("gray80","skyblue")) +
scale_shape_manual(values = c(21,8)) +
labs(x = "Posterior draws (typhoon effect)",
y = "Site") +
cowplot::theme_minimal_grid(font_size = 14,
colour = "grey92")
acoustic index temporal variability
NDSI - no effect of typhoon or land use
# get pre vs post data for comparison
dat_pre<-tidy.stability_AI %>%
filter(Index %in% "NDSI" & response_variable %in% 'Pre_Var')
dat_post<-tidy.stability_AI %>%
filter(Index %in% "NDSI" & response_variable %in% 'Post_Var')
dat_pre<-dat_pre[complete.cases(dat_pre),]
dat_post<-dat_post[complete.cases(dat_post),]
dat_pre$Typhoon = rep("Pre", nrow(dat_pre))
dat_post$Typhoon = rep("Post", nrow(dat_post))
df <- rbind(dat_pre, dat_post)
#names(df)[1] <- names(Landuse_1000)[1]
#df <- left_join(df, Landuse_1000, by = "site_id")
# change levels for model
df$Landuse<-df$Landuse %>% parse_character() %>% parse_factor(levels = c('Forest','Developed'))
df$Typhoon<-df$Typhoon %>% parse_character() %>% parse_factor(levels = c('Pre','Post'))
#df$site_id<-df$site_id %>% parse_character() %>% parse_factor(levels = c(df$site_id[order(df$PC1)]))
# fit random intercept and slope model because random slope model didn't converge well.
mod_nonspatial_beta <-
brm(data = df, family = Beta(),
Stability ~ 1 + Landuse * Typhoon + (1+Landuse*Typhoon|Site_ID),
iter = 5e4, warmup = 5000, chains = 4, cores = 4, thin = 2,
seed = 666)## Compiling Stan program...## Trying to compile a simple C file## Running /Library/Frameworks/R.framework/Resources/bin/R CMD SHLIB foo.c
## clang -mmacosx-version-min=10.13 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/Rcpp/include/" -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/" -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/unsupported" -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/BH/include" -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/StanHeaders/include/src/" -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/StanHeaders/include/" -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppParallel/include/" -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/rstan/include" -DEIGEN_NO_DEBUG -DBOOST_DISABLE_ASSERTS -DBOOST_PENDING_INTEGER_LOG2_HPP -DSTAN_THREADS -DBOOST_NO_AUTO_PTR -include '/Library/Frameworks/R.framework/Versions/4.2/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp' -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1 -I/usr/local/include -fPIC -Wall -g -O2 -c foo.c -o foo.o
## In file included from <built-in>:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.2/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
## In file included from /Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/Eigen/Dense:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/Eigen/Core:88:
## /Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:628:1: error: unknown type name 'namespace'
## namespace Eigen {
## ^
## /Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:628:16: error: expected ';' after top level declarator
## namespace Eigen {
## ^
## ;
## In file included from <built-in>:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.2/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
## In file included from /Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/Eigen/Dense:1:
## /Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/Eigen/Core:96:10: fatal error: 'complex' file not found
## #include <complex>
## ^~~~~~~~~
## 3 errors generated.
## make: *** [foo.o] Error 1## Start sampling## Warning: There were 566 divergent transitions after warmup. See
## https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## to find out why this is a problem and how to eliminate them.## Warning: Examine the pairs() plot to diagnose sampling problemsmod_nonspatial_beta <- add_criterion(mod_nonspatial_beta, "loo")## Warning: Found 35 observations with a pareto_k > 0.7 in model
## 'mod_nonspatial_beta'. It is recommended to set 'moment_match = TRUE' in order
## to perform moment matching for problematic observations.#check MCMC traces
mcmc_plot(mod_nonspatial_beta, type = "trace") + theme_cowplot()
#check for agreement of chains
mcmc_plot(mod_nonspatial_beta, type = "dens_overlay") + theme_cowplot()
# plot posterior estimates of fixed effects
mcmc_plot(mod_nonspatial_beta, type = "intervals",prob = 0.68, prob_outer = 0.95, variable = "^b_", regex = TRUE) + theme_cowplot()
## no effects ##
## Parameter fits and stats
mod_nonspatial_beta$fit # Rhat values closer to 1 and n_eff values > 1000 are ideal.## Inference for Stan model: 1b3a3d37e3bf884746f549577180da75.
## 4 chains, each with iter=50000; warmup=5000; thin=2;
## post-warmup draws per chain=22500, total post-warmup draws=90000.
##
## mean se_mean
## b_Intercept -0.16 0.00
## b_LanduseDeveloped 0.36 0.00
## b_TyphoonPost -0.10 0.00
## b_LanduseDeveloped:TyphoonPost -0.22 0.00
## sd_Site_ID__Intercept 1.24 0.00
## sd_Site_ID__LanduseDeveloped 0.84 0.01
## sd_Site_ID__TyphoonPost 0.98 0.01
## sd_Site_ID__LanduseDeveloped:TyphoonPost 1.48 0.00
## cor_Site_ID__Intercept__LanduseDeveloped -0.40 0.00
## cor_Site_ID__Intercept__TyphoonPost -0.31 0.00
## cor_Site_ID__LanduseDeveloped__TyphoonPost 0.20 0.00
## cor_Site_ID__Intercept__LanduseDeveloped:TyphoonPost 0.16 0.00
## cor_Site_ID__LanduseDeveloped__LanduseDeveloped:TyphoonPost 0.04 0.00
## cor_Site_ID__TyphoonPost__LanduseDeveloped:TyphoonPost -0.05 0.00
## phi 11.42 0.18
## r_Site_ID[CHATANOP,Intercept] -0.15 0.00
## r_Site_ID[GENKAOP,Intercept] -0.16 0.01
## r_Site_ID[GESASHIOP,Intercept] 0.84 0.01
## r_Site_ID[HEIWAOP,Intercept] -3.77 0.01
## r_Site_ID[HENTONAOP,Intercept] 0.56 0.01
## r_Site_ID[KATSUUDAKEFR,Intercept] -0.16 0.01
## r_Site_ID[KEMINFR,Intercept] -0.34 0.01
## r_Site_ID[KURASHIKIOP,Intercept] 0.78 0.00
## r_Site_ID[MANABIFR,Intercept] 0.35 0.01
## r_Site_ID[NAGOFR,Intercept] 0.78 0.00
## r_Site_ID[NAKAGUSUKUFR,Intercept] 0.03 0.00
## r_Site_ID[OISTFR,Intercept] 0.05 0.00
## r_Site_ID[OISTOP,Intercept] -0.57 0.02
## r_Site_ID[OKUFR,Intercept] -0.33 0.01
## r_Site_ID[OYAMAOP,Intercept] 0.05 0.01
## r_Site_ID[SEFAFR,Intercept] 0.31 0.00
## r_Site_ID[SENBARUOP,Intercept] 0.55 0.00
## r_Site_ID[SUEYOSHIFR,Intercept] 0.89 0.00
## r_Site_ID[TAKEYANBARUF,Intercept] 0.26 0.01
## r_Site_ID[TAMAGUSUKUFR,Intercept] -0.15 0.00
## r_Site_ID[TOUNANOP,Intercept] 0.40 0.01
## r_Site_ID[UEHARAFR,Intercept] -0.36 0.00
## r_Site_ID[YACHOOP,Intercept] -0.22 0.00
## r_Site_ID[YONAFR,Intercept] 0.30 0.01
## r_Site_ID[CHATANOP,LanduseDeveloped] 0.01 0.01
## r_Site_ID[GENKAOP,LanduseDeveloped] 0.19 0.01
## r_Site_ID[GESASHIOP,LanduseDeveloped] -0.36 0.01
## r_Site_ID[HEIWAOP,LanduseDeveloped] 1.37 0.01
## r_Site_ID[HENTONAOP,LanduseDeveloped] 0.24 0.01
## r_Site_ID[KATSUUDAKEFR,LanduseDeveloped] -0.04 0.01
## r_Site_ID[KEMINFR,LanduseDeveloped] 0.00 0.01
## r_Site_ID[KURASHIKIOP,LanduseDeveloped] -0.27 0.01
## r_Site_ID[MANABIFR,LanduseDeveloped] 0.11 0.01
## r_Site_ID[NAGOFR,LanduseDeveloped] -0.25 0.00
## r_Site_ID[NAKAGUSUKUFR,LanduseDeveloped] 0.03 0.00
## r_Site_ID[OISTFR,LanduseDeveloped] -0.06 0.00
## r_Site_ID[OISTOP,LanduseDeveloped] -0.62 0.02
## r_Site_ID[OKUFR,LanduseDeveloped] -0.18 0.01
## r_Site_ID[OYAMAOP,LanduseDeveloped] -0.14 0.01
## r_Site_ID[SEFAFR,LanduseDeveloped] -0.06 0.00
## r_Site_ID[SENBARUOP,LanduseDeveloped] -0.21 0.01
## r_Site_ID[SUEYOSHIFR,LanduseDeveloped] -0.33 0.00
## r_Site_ID[TAKEYANBARUF,LanduseDeveloped] 0.23 0.00
## r_Site_ID[TAMAGUSUKUFR,LanduseDeveloped] 0.14 0.01
## r_Site_ID[TOUNANOP,LanduseDeveloped] -0.24 0.01
## r_Site_ID[UEHARAFR,LanduseDeveloped] 0.19 0.00
## r_Site_ID[YACHOOP,LanduseDeveloped] 0.11 0.00
## r_Site_ID[YONAFR,LanduseDeveloped] 0.14 0.00
## r_Site_ID[CHATANOP,TyphoonPost] -0.41 0.00
## r_Site_ID[GENKAOP,TyphoonPost] 0.61 0.01
## r_Site_ID[GESASHIOP,TyphoonPost] -0.79 0.01
## r_Site_ID[HEIWAOP,TyphoonPost] 0.94 0.01
## r_Site_ID[HENTONAOP,TyphoonPost] 0.02 0.00
## r_Site_ID[KATSUUDAKEFR,TyphoonPost] 0.08 0.01
## r_Site_ID[KEMINFR,TyphoonPost] 0.29 0.01
## r_Site_ID[KURASHIKIOP,TyphoonPost] 0.01 0.00
## r_Site_ID[MANABIFR,TyphoonPost] -0.12 0.00
## r_Site_ID[NAGOFR,TyphoonPost] 0.16 0.00
## r_Site_ID[NAKAGUSUKUFR,TyphoonPost] 0.36 0.00
## r_Site_ID[OISTFR,TyphoonPost] -0.20 0.00
## r_Site_ID[OISTOP,TyphoonPost] -0.95 0.01
## r_Site_ID[OKUFR,TyphoonPost] -0.13 0.00
## r_Site_ID[OYAMAOP,TyphoonPost] -1.16 0.01
## r_Site_ID[SEFAFR,TyphoonPost] 0.43 0.00
## r_Site_ID[SENBARUOP,TyphoonPost] -0.17 0.00
## r_Site_ID[SUEYOSHIFR,TyphoonPost] -0.27 0.00
## r_Site_ID[TAKEYANBARUF,TyphoonPost] 0.31 0.01
## r_Site_ID[TAMAGUSUKUFR,TyphoonPost] 0.94 0.01
## r_Site_ID[TOUNANOP,TyphoonPost] -1.02 0.01
## r_Site_ID[UEHARAFR,TyphoonPost] 0.63 0.00
## r_Site_ID[YACHOOP,TyphoonPost] 0.34 0.00
## r_Site_ID[YONAFR,TyphoonPost] 0.07 0.00
## r_Site_ID[CHATANOP,LanduseDeveloped:TyphoonPost] 0.01 0.01
## r_Site_ID[GENKAOP,LanduseDeveloped:TyphoonPost] 1.12 0.01
## r_Site_ID[GESASHIOP,LanduseDeveloped:TyphoonPost] 0.25 0.01
## r_Site_ID[HEIWAOP,LanduseDeveloped:TyphoonPost] -0.87 0.01
## r_Site_ID[HENTONAOP,LanduseDeveloped:TyphoonPost] 0.46 0.00
## r_Site_ID[KATSUUDAKEFR,LanduseDeveloped:TyphoonPost] 0.03 0.01
## r_Site_ID[KEMINFR,LanduseDeveloped:TyphoonPost] 0.33 0.00
## r_Site_ID[KURASHIKIOP,LanduseDeveloped:TyphoonPost] 0.15 0.01
## r_Site_ID[MANABIFR,LanduseDeveloped:TyphoonPost] 0.04 0.00
## r_Site_ID[NAGOFR,LanduseDeveloped:TyphoonPost] 0.14 0.01
## r_Site_ID[NAKAGUSUKUFR,LanduseDeveloped:TyphoonPost] -0.03 0.01
## r_Site_ID[OISTFR,LanduseDeveloped:TyphoonPost] -0.37 0.00
## r_Site_ID[OISTOP,LanduseDeveloped:TyphoonPost] -2.32 0.01
## r_Site_ID[OKUFR,LanduseDeveloped:TyphoonPost] -0.51 0.00
## r_Site_ID[OYAMAOP,LanduseDeveloped:TyphoonPost] 0.12 0.01
## r_Site_ID[SEFAFR,LanduseDeveloped:TyphoonPost] 0.01 0.01
## r_Site_ID[SENBARUOP,LanduseDeveloped:TyphoonPost] 0.12 0.01
## r_Site_ID[SUEYOSHIFR,LanduseDeveloped:TyphoonPost] 0.21 0.01
## r_Site_ID[TAKEYANBARUF,LanduseDeveloped:TyphoonPost] 0.84 0.01
## r_Site_ID[TAMAGUSUKUFR,LanduseDeveloped:TyphoonPost] -0.12 0.01
## r_Site_ID[TOUNANOP,LanduseDeveloped:TyphoonPost] 0.18 0.01
## r_Site_ID[UEHARAFR,LanduseDeveloped:TyphoonPost] -0.12 0.01
## r_Site_ID[YACHOOP,LanduseDeveloped:TyphoonPost] -0.07 0.01
## r_Site_ID[YONAFR,LanduseDeveloped:TyphoonPost] 0.36 0.00
## lprior -17.86 0.02
## lp__ -97.71 0.24
## sd 2.5%
## b_Intercept 0.39 -0.93
## b_LanduseDeveloped 0.59 -0.80
## b_TyphoonPost 0.40 -0.91
## b_LanduseDeveloped:TyphoonPost 0.80 -1.82
## sd_Site_ID__Intercept 0.31 0.75
## sd_Site_ID__LanduseDeveloped 0.60 0.04
## sd_Site_ID__TyphoonPost 0.50 0.07
## sd_Site_ID__LanduseDeveloped:TyphoonPost 0.85 0.11
## cor_Site_ID__Intercept__LanduseDeveloped 0.43 -0.95
## cor_Site_ID__Intercept__TyphoonPost 0.34 -0.82
## cor_Site_ID__LanduseDeveloped__TyphoonPost 0.43 -0.70
## cor_Site_ID__Intercept__LanduseDeveloped:TyphoonPost 0.43 -0.72
## cor_Site_ID__LanduseDeveloped__LanduseDeveloped:TyphoonPost 0.44 -0.79
## cor_Site_ID__TyphoonPost__LanduseDeveloped:TyphoonPost 0.44 -0.82
## phi 8.26 3.87
## r_Site_ID[CHATANOP,Intercept] 0.63 -1.41
## r_Site_ID[GENKAOP,Intercept] 1.06 -2.50
## r_Site_ID[GESASHIOP,Intercept] 0.69 -0.48
## r_Site_ID[HEIWAOP,Intercept] 0.92 -5.72
## r_Site_ID[HENTONAOP,Intercept] 1.03 -1.67
## r_Site_ID[KATSUUDAKEFR,Intercept] 0.94 -2.02
## r_Site_ID[KEMINFR,Intercept] 0.96 -2.30
## r_Site_ID[KURASHIKIOP,Intercept] 0.64 -0.44
## r_Site_ID[MANABIFR,Intercept] 0.95 -1.62
## r_Site_ID[NAGOFR,Intercept] 0.64 -0.45
## r_Site_ID[NAKAGUSUKUFR,Intercept] 0.63 -1.22
## r_Site_ID[OISTFR,Intercept] 0.93 -1.76
## r_Site_ID[OISTOP,Intercept] 1.45 -3.04
## r_Site_ID[OKUFR,Intercept] 0.98 -2.19
## r_Site_ID[OYAMAOP,Intercept] 0.70 -1.34
## r_Site_ID[SEFAFR,Intercept] 0.63 -0.92
## r_Site_ID[SENBARUOP,Intercept] 0.63 -0.69
## r_Site_ID[SUEYOSHIFR,Intercept] 0.65 -0.36
## r_Site_ID[TAKEYANBARUF,Intercept] 1.00 -1.90
## r_Site_ID[TAMAGUSUKUFR,Intercept] 0.67 -1.48
## r_Site_ID[TOUNANOP,Intercept] 0.69 -0.94
## r_Site_ID[UEHARAFR,Intercept] 0.66 -1.67
## r_Site_ID[YACHOOP,Intercept] 0.63 -1.48
## r_Site_ID[YONAFR,Intercept] 0.95 -1.68
## r_Site_ID[CHATANOP,LanduseDeveloped] 0.81 -1.72
## r_Site_ID[GENKAOP,LanduseDeveloped] 0.93 -1.64
## r_Site_ID[GESASHIOP,LanduseDeveloped] 0.91 -2.57
## r_Site_ID[HEIWAOP,LanduseDeveloped] 1.84 -1.20
## r_Site_ID[HENTONAOP,LanduseDeveloped] 0.92 -1.40
## r_Site_ID[KATSUUDAKEFR,LanduseDeveloped] 0.81 -1.92
## r_Site_ID[KEMINFR,LanduseDeveloped] 0.84 -1.87
## r_Site_ID[KURASHIKIOP,LanduseDeveloped] 0.85 -2.26
## r_Site_ID[MANABIFR,LanduseDeveloped] 0.84 -1.56
## r_Site_ID[NAGOFR,LanduseDeveloped] 0.85 -2.26
## r_Site_ID[NAKAGUSUKUFR,LanduseDeveloped] 0.79 -1.66
## r_Site_ID[OISTFR,LanduseDeveloped] 0.80 -1.86
## r_Site_ID[OISTOP,LanduseDeveloped] 1.35 -4.03
## r_Site_ID[OKUFR,LanduseDeveloped] 0.86 -2.30
## r_Site_ID[OYAMAOP,LanduseDeveloped] 0.92 -2.21
## r_Site_ID[SEFAFR,LanduseDeveloped] 0.81 -1.86
## r_Site_ID[SENBARUOP,LanduseDeveloped] 0.82 -2.14
## r_Site_ID[SUEYOSHIFR,LanduseDeveloped] 0.86 -2.42
## r_Site_ID[TAKEYANBARUF,LanduseDeveloped] 0.89 -1.41
## r_Site_ID[TAMAGUSUKUFR,LanduseDeveloped] 0.89 -1.65
## r_Site_ID[TOUNANOP,LanduseDeveloped] 0.91 -2.39
## r_Site_ID[UEHARAFR,LanduseDeveloped] 0.84 -1.44
## r_Site_ID[YACHOOP,LanduseDeveloped] 0.79 -1.49
## r_Site_ID[YONAFR,LanduseDeveloped] 0.83 -1.48
## r_Site_ID[CHATANOP,TyphoonPost] 0.71 -1.94
## r_Site_ID[GENKAOP,TyphoonPost] 1.05 -1.26
## r_Site_ID[GESASHIOP,TyphoonPost] 0.82 -2.56
## r_Site_ID[HEIWAOP,TyphoonPost] 1.05 -0.96
## r_Site_ID[HENTONAOP,TyphoonPost] 0.87 -1.83
## r_Site_ID[KATSUUDAKEFR,TyphoonPost] 0.81 -1.59
## r_Site_ID[KEMINFR,TyphoonPost] 0.86 -1.35
## r_Site_ID[KURASHIKIOP,TyphoonPost] 0.67 -1.36
## r_Site_ID[MANABIFR,TyphoonPost] 0.83 -1.95
## r_Site_ID[NAGOFR,TyphoonPost] 0.68 -1.18
## r_Site_ID[NAKAGUSUKUFR,TyphoonPost] 0.69 -0.93
## r_Site_ID[OISTFR,TyphoonPost] 0.82 -1.98
## r_Site_ID[OISTOP,TyphoonPost] 1.49 -4.25
## r_Site_ID[OKUFR,TyphoonPost] 0.86 -1.95
## r_Site_ID[OYAMAOP,TyphoonPost] 0.99 -3.24
## r_Site_ID[SEFAFR,TyphoonPost] 0.72 -0.85
## r_Site_ID[SENBARUOP,TyphoonPost] 0.66 -1.57
## r_Site_ID[SUEYOSHIFR,TyphoonPost] 0.68 -1.72
## r_Site_ID[TAKEYANBARUF,TyphoonPost] 0.92 -1.51
## r_Site_ID[TAMAGUSUKUFR,TyphoonPost] 0.88 -0.43
## r_Site_ID[TOUNANOP,TyphoonPost] 0.92 -2.96
## r_Site_ID[UEHARAFR,TyphoonPost] 0.77 -0.65
## r_Site_ID[YACHOOP,TyphoonPost] 0.69 -0.95
## r_Site_ID[YONAFR,TyphoonPost] 0.82 -1.65
## r_Site_ID[CHATANOP,LanduseDeveloped:TyphoonPost] 1.52 -3.10
## r_Site_ID[GENKAOP,LanduseDeveloped:TyphoonPost] 1.21 -0.75
## r_Site_ID[GESASHIOP,LanduseDeveloped:TyphoonPost] 1.65 -2.99
## r_Site_ID[HEIWAOP,LanduseDeveloped:TyphoonPost] 2.85 -7.18
## r_Site_ID[HENTONAOP,LanduseDeveloped:TyphoonPost] 1.07 -1.56
## r_Site_ID[KATSUUDAKEFR,LanduseDeveloped:TyphoonPost] 0.99 -2.04
## r_Site_ID[KEMINFR,LanduseDeveloped:TyphoonPost] 1.02 -1.62
## r_Site_ID[KURASHIKIOP,LanduseDeveloped:TyphoonPost] 1.58 -3.19
## r_Site_ID[MANABIFR,LanduseDeveloped:TyphoonPost] 1.00 -2.07
## r_Site_ID[NAGOFR,LanduseDeveloped:TyphoonPost] 1.59 -3.26
## r_Site_ID[NAKAGUSUKUFR,LanduseDeveloped:TyphoonPost] 1.52 -3.39
## r_Site_ID[OISTFR,LanduseDeveloped:TyphoonPost] 1.01 -2.67
## r_Site_ID[OISTOP,LanduseDeveloped:TyphoonPost] 1.70 -6.02
## r_Site_ID[OKUFR,LanduseDeveloped:TyphoonPost] 1.05 -2.89
## r_Site_ID[OYAMAOP,LanduseDeveloped:TyphoonPost] 1.78 -3.31
## r_Site_ID[SEFAFR,LanduseDeveloped:TyphoonPost] 1.55 -3.43
## r_Site_ID[SENBARUOP,LanduseDeveloped:TyphoonPost] 1.53 -3.03
## r_Site_ID[SUEYOSHIFR,LanduseDeveloped:TyphoonPost] 1.59 -3.08
## r_Site_ID[TAKEYANBARUF,LanduseDeveloped:TyphoonPost] 1.12 -1.05
## r_Site_ID[TAMAGUSUKUFR,LanduseDeveloped:TyphoonPost] 1.68 -3.93
## r_Site_ID[TOUNANOP,LanduseDeveloped:TyphoonPost] 1.70 -3.07
## r_Site_ID[UEHARAFR,LanduseDeveloped:TyphoonPost] 1.57 -3.65
## r_Site_ID[YACHOOP,LanduseDeveloped:TyphoonPost] 1.51 -3.37
## r_Site_ID[YONAFR,LanduseDeveloped:TyphoonPost] 1.01 -1.60
## lprior 1.21 -20.66
## lp__ 15.67 -124.95
## 25% 50%
## b_Intercept -0.41 -0.16
## b_LanduseDeveloped -0.02 0.36
## b_TyphoonPost -0.36 -0.10
## b_LanduseDeveloped:TyphoonPost -0.74 -0.23
## sd_Site_ID__Intercept 1.03 1.21
## sd_Site_ID__LanduseDeveloped 0.36 0.74
## sd_Site_ID__TyphoonPost 0.61 0.98
## sd_Site_ID__LanduseDeveloped:TyphoonPost 0.87 1.41
## cor_Site_ID__Intercept__LanduseDeveloped -0.76 -0.51
## cor_Site_ID__Intercept__TyphoonPost -0.56 -0.36
## cor_Site_ID__LanduseDeveloped__TyphoonPost -0.10 0.26
## cor_Site_ID__Intercept__LanduseDeveloped:TyphoonPost -0.16 0.19
## cor_Site_ID__LanduseDeveloped__LanduseDeveloped:TyphoonPost -0.30 0.05
## cor_Site_ID__TyphoonPost__LanduseDeveloped:TyphoonPost -0.39 -0.06
## phi 6.34 8.83
## r_Site_ID[CHATANOP,Intercept] -0.55 -0.14
## r_Site_ID[GENKAOP,Intercept] -0.77 -0.10
## r_Site_ID[GESASHIOP,Intercept] 0.37 0.83
## r_Site_ID[HEIWAOP,Intercept] -4.36 -3.71
## r_Site_ID[HENTONAOP,Intercept] -0.02 0.60
## r_Site_ID[KATSUUDAKEFR,Intercept] -0.73 -0.16
## r_Site_ID[KEMINFR,Intercept] -0.91 -0.33
## r_Site_ID[KURASHIKIOP,Intercept] 0.36 0.77
## r_Site_ID[MANABIFR,Intercept] -0.22 0.36
## r_Site_ID[NAGOFR,Intercept] 0.36 0.77
## r_Site_ID[NAKAGUSUKUFR,Intercept] -0.38 0.02
## r_Site_ID[OISTFR,Intercept] -0.51 0.04
## r_Site_ID[OISTOP,Intercept] -1.50 -0.74
## r_Site_ID[OKUFR,Intercept] -0.94 -0.37
## r_Site_ID[OYAMAOP,Intercept] -0.40 0.07
## r_Site_ID[SEFAFR,Intercept] -0.10 0.30
## r_Site_ID[SENBARUOP,Intercept] 0.13 0.54
## r_Site_ID[SUEYOSHIFR,Intercept] 0.45 0.88
## r_Site_ID[TAKEYANBARUF,Intercept] -0.32 0.31
## r_Site_ID[TAMAGUSUKUFR,Intercept] -0.60 -0.15
## r_Site_ID[TOUNANOP,Intercept] -0.06 0.40
## r_Site_ID[UEHARAFR,Intercept] -0.78 -0.35
## r_Site_ID[YACHOOP,Intercept] -0.62 -0.21
## r_Site_ID[YONAFR,Intercept] -0.26 0.32
## r_Site_ID[CHATANOP,LanduseDeveloped] -0.32 0.00
## r_Site_ID[GENKAOP,LanduseDeveloped] -0.23 0.07
## r_Site_ID[GESASHIOP,LanduseDeveloped] -0.76 -0.17
## r_Site_ID[HEIWAOP,LanduseDeveloped] 0.05 0.89
## r_Site_ID[HENTONAOP,LanduseDeveloped] -0.20 0.07
## r_Site_ID[KATSUUDAKEFR,LanduseDeveloped] -0.37 -0.01
## r_Site_ID[KEMINFR,LanduseDeveloped] -0.34 0.00
## r_Site_ID[KURASHIKIOP,LanduseDeveloped] -0.63 -0.12
## r_Site_ID[MANABIFR,LanduseDeveloped] -0.26 0.02
## r_Site_ID[NAGOFR,LanduseDeveloped] -0.61 -0.11
## r_Site_ID[NAKAGUSUKUFR,LanduseDeveloped] -0.30 0.01
## r_Site_ID[OISTFR,LanduseDeveloped] -0.39 -0.02
## r_Site_ID[OISTOP,LanduseDeveloped] -1.21 -0.27
## r_Site_ID[OKUFR,LanduseDeveloped] -0.51 -0.05
## r_Site_ID[OYAMAOP,LanduseDeveloped] -0.52 -0.04
## r_Site_ID[SEFAFR,LanduseDeveloped] -0.39 -0.02
## r_Site_ID[SENBARUOP,LanduseDeveloped] -0.55 -0.08
## r_Site_ID[SUEYOSHIFR,LanduseDeveloped] -0.71 -0.15
## r_Site_ID[TAKEYANBARUF,LanduseDeveloped] -0.19 0.08
## r_Site_ID[TAMAGUSUKUFR,LanduseDeveloped] -0.24 0.05
## r_Site_ID[TOUNANOP,LanduseDeveloped] -0.61 -0.09
## r_Site_ID[UEHARAFR,LanduseDeveloped] -0.19 0.07
## r_Site_ID[YACHOOP,LanduseDeveloped] -0.23 0.04
## r_Site_ID[YONAFR,LanduseDeveloped] -0.23 0.04
## r_Site_ID[CHATANOP,TyphoonPost] -0.84 -0.31
## r_Site_ID[GENKAOP,TyphoonPost] -0.04 0.44
## r_Site_ID[GESASHIOP,TyphoonPost] -1.35 -0.70
## r_Site_ID[HEIWAOP,TyphoonPost] 0.16 0.87
## r_Site_ID[HENTONAOP,TyphoonPost] -0.43 0.01
## r_Site_ID[KATSUUDAKEFR,TyphoonPost] -0.34 0.04
## r_Site_ID[KEMINFR,TyphoonPost] -0.17 0.18
## r_Site_ID[KURASHIKIOP,TyphoonPost] -0.38 0.00
## r_Site_ID[MANABIFR,TyphoonPost] -0.54 -0.06
## r_Site_ID[NAGOFR,TyphoonPost] -0.24 0.10
## r_Site_ID[NAKAGUSUKUFR,TyphoonPost] -0.07 0.27
## r_Site_ID[OISTFR,TyphoonPost] -0.65 -0.12
## r_Site_ID[OISTOP,TyphoonPost] -1.88 -0.69
## r_Site_ID[OKUFR,TyphoonPost] -0.58 -0.07
## r_Site_ID[OYAMAOP,TyphoonPost] -1.86 -1.07
## r_Site_ID[SEFAFR,TyphoonPost] -0.03 0.34
## r_Site_ID[SENBARUOP,TyphoonPost] -0.55 -0.11
## r_Site_ID[SUEYOSHIFR,TyphoonPost] -0.68 -0.20
## r_Site_ID[TAKEYANBARUF,TyphoonPost] -0.20 0.20
## r_Site_ID[TAMAGUSUKUFR,TyphoonPost] 0.20 0.85
## r_Site_ID[TOUNANOP,TyphoonPost] -1.66 -0.93
## r_Site_ID[UEHARAFR,TyphoonPost] 0.05 0.53
## r_Site_ID[YACHOOP,TyphoonPost] -0.08 0.26
## r_Site_ID[YONAFR,TyphoonPost] -0.36 0.04
## r_Site_ID[CHATANOP,LanduseDeveloped:TyphoonPost] -0.72 -0.02
## r_Site_ID[GENKAOP,LanduseDeveloped:TyphoonPost] 0.24 0.95
## r_Site_ID[GESASHIOP,LanduseDeveloped:TyphoonPost] -0.57 0.09
## r_Site_ID[HEIWAOP,LanduseDeveloped:TyphoonPost] -2.34 -0.55
## r_Site_ID[HENTONAOP,LanduseDeveloped:TyphoonPost] -0.14 0.34
## r_Site_ID[KATSUUDAKEFR,LanduseDeveloped:TyphoonPost] -0.49 0.01
## r_Site_ID[KEMINFR,LanduseDeveloped:TyphoonPost] -0.24 0.21
## r_Site_ID[KURASHIKIOP,LanduseDeveloped:TyphoonPost] -0.58 0.09
## r_Site_ID[MANABIFR,LanduseDeveloped:TyphoonPost] -0.48 0.03
## r_Site_ID[NAGOFR,LanduseDeveloped:TyphoonPost] -0.60 0.09
## r_Site_ID[NAKAGUSUKUFR,LanduseDeveloped:TyphoonPost] -0.72 0.01
## r_Site_ID[OISTFR,LanduseDeveloped:TyphoonPost] -0.90 -0.25
## r_Site_ID[OISTOP,LanduseDeveloped:TyphoonPost] -3.40 -2.20
## r_Site_ID[OKUFR,LanduseDeveloped:TyphoonPost] -1.08 -0.38
## r_Site_ID[OYAMAOP,LanduseDeveloped:TyphoonPost] -0.78 0.00
## r_Site_ID[SEFAFR,LanduseDeveloped:TyphoonPost] -0.68 0.03
## r_Site_ID[SENBARUOP,LanduseDeveloped:TyphoonPost] -0.60 0.06
## r_Site_ID[SUEYOSHIFR,LanduseDeveloped:TyphoonPost] -0.54 0.11
## r_Site_ID[TAKEYANBARUF,LanduseDeveloped:TyphoonPost] 0.07 0.68
## r_Site_ID[TAMAGUSUKUFR,LanduseDeveloped:TyphoonPost] -0.84 0.00
## r_Site_ID[TOUNANOP,LanduseDeveloped:TyphoonPost] -0.67 0.03
## r_Site_ID[UEHARAFR,LanduseDeveloped:TyphoonPost] -0.83 -0.03
## r_Site_ID[YACHOOP,LanduseDeveloped:TyphoonPost] -0.76 -0.02
## r_Site_ID[YONAFR,LanduseDeveloped:TyphoonPost] -0.19 0.26
## lprior -18.57 -17.67
## lp__ -108.76 -99.09
## 75% 97.5% n_eff
## b_Intercept 0.10 0.62 12861
## b_LanduseDeveloped 0.75 1.53 53362
## b_TyphoonPost 0.16 0.69 32442
## b_LanduseDeveloped:TyphoonPost 0.29 1.38 43285
## sd_Site_ID__Intercept 1.42 1.94 4289
## sd_Site_ID__LanduseDeveloped 1.22 2.23 8835
## sd_Site_ID__TyphoonPost 1.32 1.97 6456
## sd_Site_ID__LanduseDeveloped:TyphoonPost 2.00 3.37 32776
## cor_Site_ID__Intercept__LanduseDeveloped -0.13 0.62 35193
## cor_Site_ID__Intercept__TyphoonPost -0.11 0.49 48553
## cor_Site_ID__LanduseDeveloped__TyphoonPost 0.55 0.87 34255
## cor_Site_ID__Intercept__LanduseDeveloped:TyphoonPost 0.50 0.87 38891
## cor_Site_ID__LanduseDeveloped__LanduseDeveloped:TyphoonPost 0.38 0.82 48473
## cor_Site_ID__TyphoonPost__LanduseDeveloped:TyphoonPost 0.29 0.78 53036
## phi 13.46 34.80 2100
## r_Site_ID[CHATANOP,Intercept] 0.26 1.08 26017
## r_Site_ID[GENKAOP,Intercept] 0.52 1.83 7556
## r_Site_ID[GESASHIOP,Intercept] 1.29 2.23 16121
## r_Site_ID[HEIWAOP,Intercept] -3.11 -2.13 11073
## r_Site_ID[HENTONAOP,Intercept] 1.20 2.52 9148
## r_Site_ID[KATSUUDAKEFR,Intercept] 0.39 1.76 10281
## r_Site_ID[KEMINFR,Intercept] 0.25 1.59 21222
## r_Site_ID[KURASHIKIOP,Intercept] 1.19 2.09 20777
## r_Site_ID[MANABIFR,Intercept] 0.93 2.26 26248
## r_Site_ID[NAGOFR,Intercept] 1.19 2.08 33055
## r_Site_ID[NAKAGUSUKUFR,Intercept] 0.43 1.28 30445
## r_Site_ID[OISTFR,Intercept] 0.61 1.98 37816
## r_Site_ID[OISTOP,Intercept] 0.19 2.82 5421
## r_Site_ID[OKUFR,Intercept] 0.23 1.76 12033
## r_Site_ID[OYAMAOP,Intercept] 0.52 1.40 18103
## r_Site_ID[SEFAFR,Intercept] 0.71 1.59 34715
## r_Site_ID[SENBARUOP,Intercept] 0.95 1.83 16810
## r_Site_ID[SUEYOSHIFR,Intercept] 1.31 2.20 22636
## r_Site_ID[TAKEYANBARUF,Intercept] 0.89 2.15 36589
## r_Site_ID[TAMAGUSUKUFR,Intercept] 0.30 1.18 32927
## r_Site_ID[TOUNANOP,Intercept] 0.85 1.75 13779
## r_Site_ID[UEHARAFR,Intercept] 0.08 0.92 44021
## r_Site_ID[YACHOOP,Intercept] 0.20 1.02 36736
## r_Site_ID[YONAFR,Intercept] 0.88 2.15 31695
## r_Site_ID[CHATANOP,LanduseDeveloped] 0.34 1.72 9841
## r_Site_ID[GENKAOP,LanduseDeveloped] 0.59 2.43 7086
## r_Site_ID[GESASHIOP,LanduseDeveloped] 0.10 1.23 32061
## r_Site_ID[HEIWAOP,LanduseDeveloped] 2.39 5.80 15355
## r_Site_ID[HENTONAOP,LanduseDeveloped] 0.59 2.54 5480
## r_Site_ID[KATSUUDAKEFR,LanduseDeveloped] 0.31 1.64 7431
## r_Site_ID[KEMINFR,LanduseDeveloped] 0.35 1.80 13956
## r_Site_ID[KURASHIKIOP,LanduseDeveloped] 0.13 1.31 18217
## r_Site_ID[MANABIFR,LanduseDeveloped] 0.43 2.11 14646
## r_Site_ID[NAGOFR,LanduseDeveloped] 0.14 1.34 43687
## r_Site_ID[NAKAGUSUKUFR,LanduseDeveloped] 0.36 1.73 40525
## r_Site_ID[OISTFR,LanduseDeveloped] 0.28 1.63 43012
## r_Site_ID[OISTOP,LanduseDeveloped] 0.13 1.55 5824
## r_Site_ID[OKUFR,LanduseDeveloped] 0.21 1.45 10627
## r_Site_ID[OYAMAOP,LanduseDeveloped] 0.27 1.69 29461
## r_Site_ID[SEFAFR,LanduseDeveloped] 0.29 1.63 38704
## r_Site_ID[SENBARUOP,LanduseDeveloped] 0.16 1.34 12261
## r_Site_ID[SUEYOSHIFR,LanduseDeveloped] 0.10 1.21 41514
## r_Site_ID[TAKEYANBARUF,LanduseDeveloped] 0.60 2.39 32582
## r_Site_ID[TAMAGUSUKUFR,LanduseDeveloped] 0.51 2.18 11728
## r_Site_ID[TOUNANOP,LanduseDeveloped] 0.18 1.45 12899
## r_Site_ID[UEHARAFR,LanduseDeveloped] 0.53 2.12 32144
## r_Site_ID[YACHOOP,LanduseDeveloped] 0.43 1.92 37233
## r_Site_ID[YONAFR,LanduseDeveloped] 0.46 2.11 36780
## r_Site_ID[CHATANOP,TyphoonPost] 0.05 0.87 33622
## r_Site_ID[GENKAOP,TyphoonPost] 1.23 2.94 17802
## r_Site_ID[GESASHIOP,TyphoonPost] -0.14 0.53 18177
## r_Site_ID[HEIWAOP,TyphoonPost] 1.66 3.09 24222
## r_Site_ID[HENTONAOP,TyphoonPost] 0.49 1.82 46330
## r_Site_ID[KATSUUDAKEFR,TyphoonPost] 0.50 1.80 20197
## r_Site_ID[KEMINFR,TyphoonPost] 0.76 2.16 27451
## r_Site_ID[KURASHIKIOP,TyphoonPost] 0.39 1.41 49377
## r_Site_ID[MANABIFR,TyphoonPost] 0.32 1.54 36066
## r_Site_ID[NAGOFR,TyphoonPost] 0.56 1.62 53703
## r_Site_ID[NAKAGUSUKUFR,TyphoonPost] 0.78 1.84 40936
## r_Site_ID[OISTFR,TyphoonPost] 0.24 1.43 34497
## r_Site_ID[OISTOP,TyphoonPost] 0.04 1.64 12401
## r_Site_ID[OKUFR,TyphoonPost] 0.32 1.61 42482
## r_Site_ID[OYAMAOP,TyphoonPost] -0.32 0.32 11062
## r_Site_ID[SEFAFR,TyphoonPost] 0.88 2.00 36300
## r_Site_ID[SENBARUOP,TyphoonPost] 0.21 1.15 36145
## r_Site_ID[SUEYOSHIFR,TyphoonPost] 0.14 1.04 40193
## r_Site_ID[TAKEYANBARUF,TyphoonPost] 0.84 2.30 23073
## r_Site_ID[TAMAGUSUKUFR,TyphoonPost] 1.55 2.81 21887
## r_Site_ID[TOUNANOP,TyphoonPost] -0.25 0.38 12083
## r_Site_ID[UEHARAFR,TyphoonPost] 1.13 2.29 30824
## r_Site_ID[YACHOOP,TyphoonPost] 0.75 1.83 40887
## r_Site_ID[YONAFR,TyphoonPost] 0.51 1.80 48586
## r_Site_ID[CHATANOP,LanduseDeveloped:TyphoonPost] 0.70 3.34 52212
## r_Site_ID[GENKAOP,LanduseDeveloped:TyphoonPost] 1.82 3.93 41462
## r_Site_ID[GESASHIOP,LanduseDeveloped:TyphoonPost] 1.00 4.05 52845
## r_Site_ID[HEIWAOP,LanduseDeveloped:TyphoonPost] 0.61 4.66 40189
## r_Site_ID[HENTONAOP,LanduseDeveloped:TyphoonPost] 1.04 2.83 53075
## r_Site_ID[KATSUUDAKEFR,LanduseDeveloped:TyphoonPost] 0.54 2.12 34538
## r_Site_ID[KEMINFR,LanduseDeveloped:TyphoonPost] 0.87 2.61 45398
## r_Site_ID[KURASHIKIOP,LanduseDeveloped:TyphoonPost] 0.90 3.53 24164
## r_Site_ID[MANABIFR,LanduseDeveloped:TyphoonPost] 0.58 2.15 50574
## r_Site_ID[NAGOFR,LanduseDeveloped:TyphoonPost] 0.89 3.51 47380
## r_Site_ID[NAKAGUSUKUFR,LanduseDeveloped:TyphoonPost] 0.70 3.05 50238
## r_Site_ID[OISTFR,LanduseDeveloped:TyphoonPost] 0.20 1.53 49965
## r_Site_ID[OISTOP,LanduseDeveloped:TyphoonPost] -1.03 0.31 29953
## r_Site_ID[OKUFR,LanduseDeveloped:TyphoonPost] 0.09 1.43 48796
## r_Site_ID[OYAMAOP,LanduseDeveloped:TyphoonPost] 0.88 4.25 34376
## r_Site_ID[SEFAFR,LanduseDeveloped:TyphoonPost] 0.77 3.17 58956
## r_Site_ID[SENBARUOP,LanduseDeveloped:TyphoonPost] 0.83 3.46 56052
## r_Site_ID[SUEYOSHIFR,LanduseDeveloped:TyphoonPost] 0.97 3.67 44075
## r_Site_ID[TAKEYANBARUF,LanduseDeveloped:TyphoonPost] 1.47 3.42 40521
## r_Site_ID[TAMAGUSUKUFR,LanduseDeveloped:TyphoonPost] 0.71 3.15 32201
## r_Site_ID[TOUNANOP,LanduseDeveloped:TyphoonPost] 0.92 4.11 47145
## r_Site_ID[UEHARAFR,LanduseDeveloped:TyphoonPost] 0.64 2.98 54907
## r_Site_ID[YACHOOP,LanduseDeveloped:TyphoonPost] 0.64 3.06 44274
## r_Site_ID[YONAFR,LanduseDeveloped:TyphoonPost] 0.90 2.61 60570
## lprior -16.96 -16.04 4873
## lp__ -87.95 -63.28 4244
## Rhat
## b_Intercept 1
## b_LanduseDeveloped 1
## b_TyphoonPost 1
## b_LanduseDeveloped:TyphoonPost 1
## sd_Site_ID__Intercept 1
## sd_Site_ID__LanduseDeveloped 1
## sd_Site_ID__TyphoonPost 1
## sd_Site_ID__LanduseDeveloped:TyphoonPost 1
## cor_Site_ID__Intercept__LanduseDeveloped 1
## cor_Site_ID__Intercept__TyphoonPost 1
## cor_Site_ID__LanduseDeveloped__TyphoonPost 1
## cor_Site_ID__Intercept__LanduseDeveloped:TyphoonPost 1
## cor_Site_ID__LanduseDeveloped__LanduseDeveloped:TyphoonPost 1
## cor_Site_ID__TyphoonPost__LanduseDeveloped:TyphoonPost 1
## phi 1
## r_Site_ID[CHATANOP,Intercept] 1
## r_Site_ID[GENKAOP,Intercept] 1
## r_Site_ID[GESASHIOP,Intercept] 1
## r_Site_ID[HEIWAOP,Intercept] 1
## r_Site_ID[HENTONAOP,Intercept] 1
## r_Site_ID[KATSUUDAKEFR,Intercept] 1
## r_Site_ID[KEMINFR,Intercept] 1
## r_Site_ID[KURASHIKIOP,Intercept] 1
## r_Site_ID[MANABIFR,Intercept] 1
## r_Site_ID[NAGOFR,Intercept] 1
## r_Site_ID[NAKAGUSUKUFR,Intercept] 1
## r_Site_ID[OISTFR,Intercept] 1
## r_Site_ID[OISTOP,Intercept] 1
## r_Site_ID[OKUFR,Intercept] 1
## r_Site_ID[OYAMAOP,Intercept] 1
## r_Site_ID[SEFAFR,Intercept] 1
## r_Site_ID[SENBARUOP,Intercept] 1
## r_Site_ID[SUEYOSHIFR,Intercept] 1
## r_Site_ID[TAKEYANBARUF,Intercept] 1
## r_Site_ID[TAMAGUSUKUFR,Intercept] 1
## r_Site_ID[TOUNANOP,Intercept] 1
## r_Site_ID[UEHARAFR,Intercept] 1
## r_Site_ID[YACHOOP,Intercept] 1
## r_Site_ID[YONAFR,Intercept] 1
## r_Site_ID[CHATANOP,LanduseDeveloped] 1
## r_Site_ID[GENKAOP,LanduseDeveloped] 1
## r_Site_ID[GESASHIOP,LanduseDeveloped] 1
## r_Site_ID[HEIWAOP,LanduseDeveloped] 1
## r_Site_ID[HENTONAOP,LanduseDeveloped] 1
## r_Site_ID[KATSUUDAKEFR,LanduseDeveloped] 1
## r_Site_ID[KEMINFR,LanduseDeveloped] 1
## r_Site_ID[KURASHIKIOP,LanduseDeveloped] 1
## r_Site_ID[MANABIFR,LanduseDeveloped] 1
## r_Site_ID[NAGOFR,LanduseDeveloped] 1
## r_Site_ID[NAKAGUSUKUFR,LanduseDeveloped] 1
## r_Site_ID[OISTFR,LanduseDeveloped] 1
## r_Site_ID[OISTOP,LanduseDeveloped] 1
## r_Site_ID[OKUFR,LanduseDeveloped] 1
## r_Site_ID[OYAMAOP,LanduseDeveloped] 1
## r_Site_ID[SEFAFR,LanduseDeveloped] 1
## r_Site_ID[SENBARUOP,LanduseDeveloped] 1
## r_Site_ID[SUEYOSHIFR,LanduseDeveloped] 1
## r_Site_ID[TAKEYANBARUF,LanduseDeveloped] 1
## r_Site_ID[TAMAGUSUKUFR,LanduseDeveloped] 1
## r_Site_ID[TOUNANOP,LanduseDeveloped] 1
## r_Site_ID[UEHARAFR,LanduseDeveloped] 1
## r_Site_ID[YACHOOP,LanduseDeveloped] 1
## r_Site_ID[YONAFR,LanduseDeveloped] 1
## r_Site_ID[CHATANOP,TyphoonPost] 1
## r_Site_ID[GENKAOP,TyphoonPost] 1
## r_Site_ID[GESASHIOP,TyphoonPost] 1
## r_Site_ID[HEIWAOP,TyphoonPost] 1
## r_Site_ID[HENTONAOP,TyphoonPost] 1
## r_Site_ID[KATSUUDAKEFR,TyphoonPost] 1
## r_Site_ID[KEMINFR,TyphoonPost] 1
## r_Site_ID[KURASHIKIOP,TyphoonPost] 1
## r_Site_ID[MANABIFR,TyphoonPost] 1
## r_Site_ID[NAGOFR,TyphoonPost] 1
## r_Site_ID[NAKAGUSUKUFR,TyphoonPost] 1
## r_Site_ID[OISTFR,TyphoonPost] 1
## r_Site_ID[OISTOP,TyphoonPost] 1
## r_Site_ID[OKUFR,TyphoonPost] 1
## r_Site_ID[OYAMAOP,TyphoonPost] 1
## r_Site_ID[SEFAFR,TyphoonPost] 1
## r_Site_ID[SENBARUOP,TyphoonPost] 1
## r_Site_ID[SUEYOSHIFR,TyphoonPost] 1
## r_Site_ID[TAKEYANBARUF,TyphoonPost] 1
## r_Site_ID[TAMAGUSUKUFR,TyphoonPost] 1
## r_Site_ID[TOUNANOP,TyphoonPost] 1
## r_Site_ID[UEHARAFR,TyphoonPost] 1
## r_Site_ID[YACHOOP,TyphoonPost] 1
## r_Site_ID[YONAFR,TyphoonPost] 1
## r_Site_ID[CHATANOP,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[GENKAOP,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[GESASHIOP,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[HEIWAOP,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[HENTONAOP,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[KATSUUDAKEFR,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[KEMINFR,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[KURASHIKIOP,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[MANABIFR,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[NAGOFR,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[NAKAGUSUKUFR,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[OISTFR,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[OISTOP,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[OKUFR,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[OYAMAOP,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[SEFAFR,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[SENBARUOP,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[SUEYOSHIFR,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[TAKEYANBARUF,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[TAMAGUSUKUFR,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[TOUNANOP,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[UEHARAFR,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[YACHOOP,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[YONAFR,LanduseDeveloped:TyphoonPost] 1
## lprior 1
## lp__ 1
##
## Samples were drawn using NUTS(diag_e) at Mon Nov 14 13:29:23 2022.
## For each parameter, n_eff is a crude measure of effective sample size,
## and Rhat is the potential scale reduction factor on split chains (at
## convergence, Rhat=1).bayes_R2(mod_nonspatial_beta) # pseudo r-squared of model## Estimate Est.Error Q2.5 Q97.5
## R2 0.7044893 0.1139198 0.49624 0.9131412# simulate data from 500 random draws of posterior and compare it to observed data
# the black line should run through the center of the blue lines
pp_check(mod_nonspatial_beta, ndraws = 500) + theme_cowplot()
## Spatial Autocorrelation test
bres <- residuals(mod_nonspatial_beta)[,"Estimate"]
d_mat = as.matrix(dist(df[,c("Lat", "Long")], diag=T, upper=T)) # make distance matrix
d_mat_inv <- 1/d_mat
d_mat_inv[which(d_mat_inv == Inf)] <- 0
Moran.I(bres, d_mat_inv) # non-significant = no spatial autocorrelation## $observed
## [1] -0.01199015
##
## $expected
## [1] -0.0212766
##
## $sd
## [1] 0.04112041
##
## $p.value
## [1] 0.8213293NDSI_Bio - no effect of typhoon or land use
# get pre vs post data for comparison
dat_pre<-tidy.stability_AI %>%
filter(Index %in% "NDSI_Bio" & response_variable %in% 'Pre_Var')
dat_post<-tidy.stability_AI %>%
filter(Index %in% "NDSI_Bio" & response_variable %in% 'Post_Var')
dat_pre<-dat_pre[complete.cases(dat_pre),]
dat_post<-dat_post[complete.cases(dat_post),]
dat_pre$Typhoon = rep("Pre", nrow(dat_pre))
dat_post$Typhoon = rep("Post", nrow(dat_post))
df <- rbind(dat_pre, dat_post)
#names(df)[1] <- names(Landuse_1000)[1]
#df <- left_join(df, Landuse_1000, by = "site_id")
# change levels for model
df$Landuse<-df$Landuse %>% parse_character() %>% parse_factor(levels = c('Forest','Developed'))
df$Typhoon<-df$Typhoon %>% parse_character() %>% parse_factor(levels = c('Pre','Post'))
#df$site_id<-df$site_id %>% parse_character() %>% parse_factor(levels = c(df$site_id[order(df$PC1)]))
# fit random intercept and slope model because random slope model didn't converge well.
mod_nonspatial_beta <-
brm(data = df, family = Beta(),
Stability ~ 1 + Landuse * Typhoon + (1+Landuse*Typhoon|Site_ID),
iter = 5e4, warmup = 5000, chains = 4, cores = 4, thin = 2,
seed = 666)## Compiling Stan program...## Start sampling## Warning: There were 111 divergent transitions after warmup. See
## https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## to find out why this is a problem and how to eliminate them.## Warning: Examine the pairs() plot to diagnose sampling problemsmod_nonspatial_beta <- add_criterion(mod_nonspatial_beta, "loo")## Warning: Found 46 observations with a pareto_k > 0.7 in model
## 'mod_nonspatial_beta'. It is recommended to set 'moment_match = TRUE' in order
## to perform moment matching for problematic observations.#check MCMC traces
mcmc_plot(mod_nonspatial_beta, type = "trace") + theme_cowplot()
#check for agreement of chains
mcmc_plot(mod_nonspatial_beta, type = "dens_overlay") + theme_cowplot()
# plot posterior estimates of fixed effects
mcmc_plot(mod_nonspatial_beta, type = "intervals",prob = 0.68, prob_outer = 0.95, variable = "^b_", regex = TRUE) + theme_cowplot()
## no effects ##
## Parameter fits and stats
mod_nonspatial_beta$fit # Rhat values closer to 1 and n_eff values > 1000 are ideal.## Inference for Stan model: 1b3a3d37e3bf884746f549577180da75.
## 4 chains, each with iter=50000; warmup=5000; thin=2;
## post-warmup draws per chain=22500, total post-warmup draws=90000.
##
## mean se_mean
## b_Intercept -0.33 0.00
## b_LanduseDeveloped 0.26 0.00
## b_TyphoonPost 0.40 0.00
## b_LanduseDeveloped:TyphoonPost -0.10 0.00
## sd_Site_ID__Intercept 1.32 0.00
## sd_Site_ID__LanduseDeveloped 0.86 0.00
## sd_Site_ID__TyphoonPost 1.68 0.00
## sd_Site_ID__LanduseDeveloped:TyphoonPost 1.53 0.01
## cor_Site_ID__Intercept__LanduseDeveloped -0.43 0.00
## cor_Site_ID__Intercept__TyphoonPost -0.62 0.00
## cor_Site_ID__LanduseDeveloped__TyphoonPost 0.30 0.00
## cor_Site_ID__Intercept__LanduseDeveloped:TyphoonPost 0.03 0.00
## cor_Site_ID__LanduseDeveloped__LanduseDeveloped:TyphoonPost 0.00 0.00
## cor_Site_ID__TyphoonPost__LanduseDeveloped:TyphoonPost -0.01 0.00
## phi 12.28 0.09
## r_Site_ID[CHATANOP,Intercept] 0.06 0.00
## r_Site_ID[GENKAOP,Intercept] -0.29 0.00
## r_Site_ID[GESASHIOP,Intercept] 0.57 0.00
## r_Site_ID[HEIWAOP,Intercept] -1.42 0.00
## r_Site_ID[HENTONAOP,Intercept] -0.01 0.00
## r_Site_ID[KATSUUDAKEFR,Intercept] 0.12 0.00
## r_Site_ID[KEMINFR,Intercept] -1.10 0.01
## r_Site_ID[KURASHIKIOP,Intercept] 0.89 0.00
## r_Site_ID[MANABIFR,Intercept] 0.34 0.00
## r_Site_ID[NAGOFR,Intercept] 0.31 0.00
## r_Site_ID[NAKAGUSUKUFR,Intercept] 0.25 0.00
## r_Site_ID[OISTFR,Intercept] 0.00 0.00
## r_Site_ID[OISTOP,Intercept] 0.48 0.01
## r_Site_ID[OKUFR,Intercept] -0.15 0.00
## r_Site_ID[OYAMAOP,Intercept] 0.75 0.00
## r_Site_ID[SEFAFR,Intercept] -0.04 0.00
## r_Site_ID[SENBARUOP,Intercept] 1.14 0.00
## r_Site_ID[SUEYOSHIFR,Intercept] 1.47 0.00
## r_Site_ID[TAKEYANBARUF,Intercept] 0.21 0.00
## r_Site_ID[TAMAGUSUKUFR,Intercept] -3.68 0.01
## r_Site_ID[TOUNANOP,Intercept] 0.81 0.00
## r_Site_ID[UEHARAFR,Intercept] -0.33 0.00
## r_Site_ID[YACHOOP,Intercept] -0.78 0.00
## r_Site_ID[YONAFR,Intercept] 0.43 0.00
## r_Site_ID[CHATANOP,LanduseDeveloped] -0.04 0.00
## r_Site_ID[GENKAOP,LanduseDeveloped] 0.25 0.00
## r_Site_ID[GESASHIOP,LanduseDeveloped] -0.20 0.00
## r_Site_ID[HEIWAOP,LanduseDeveloped] 0.49 0.00
## r_Site_ID[HENTONAOP,LanduseDeveloped] 0.21 0.00
## r_Site_ID[KATSUUDAKEFR,LanduseDeveloped] -0.01 0.00
## r_Site_ID[KEMINFR,LanduseDeveloped] -0.05 0.00
## r_Site_ID[KURASHIKIOP,LanduseDeveloped] -0.33 0.00
## r_Site_ID[MANABIFR,LanduseDeveloped] 0.10 0.00
## r_Site_ID[NAGOFR,LanduseDeveloped] -0.09 0.00
## r_Site_ID[NAKAGUSUKUFR,LanduseDeveloped] -0.09 0.00
## r_Site_ID[OISTFR,LanduseDeveloped] -0.08 0.00
## r_Site_ID[OISTOP,LanduseDeveloped] -0.77 0.01
## r_Site_ID[OKUFR,LanduseDeveloped] -0.15 0.00
## r_Site_ID[OYAMAOP,LanduseDeveloped] -0.30 0.00
## r_Site_ID[SEFAFR,LanduseDeveloped] 0.04 0.00
## r_Site_ID[SENBARUOP,LanduseDeveloped] -0.42 0.00
## r_Site_ID[SUEYOSHIFR,LanduseDeveloped] -0.51 0.00
## r_Site_ID[TAKEYANBARUF,LanduseDeveloped] 0.28 0.00
## r_Site_ID[TAMAGUSUKUFR,LanduseDeveloped] 1.37 0.01
## r_Site_ID[TOUNANOP,LanduseDeveloped] -0.33 0.00
## r_Site_ID[UEHARAFR,LanduseDeveloped] 0.11 0.00
## r_Site_ID[YACHOOP,LanduseDeveloped] 0.29 0.00
## r_Site_ID[YONAFR,LanduseDeveloped] 0.21 0.00
## r_Site_ID[CHATANOP,TyphoonPost] -1.02 0.00
## r_Site_ID[GENKAOP,TyphoonPost] 0.88 0.01
## r_Site_ID[GESASHIOP,TyphoonPost] 0.07 0.00
## r_Site_ID[HEIWAOP,TyphoonPost] -0.18 0.00
## r_Site_ID[HENTONAOP,TyphoonPost] 0.48 0.01
## r_Site_ID[KATSUUDAKEFR,TyphoonPost] -0.17 0.00
## r_Site_ID[KEMINFR,TyphoonPost] 1.23 0.01
## r_Site_ID[KURASHIKIOP,TyphoonPost] -0.73 0.00
## r_Site_ID[MANABIFR,TyphoonPost] -0.20 0.00
## r_Site_ID[NAGOFR,TyphoonPost] 0.57 0.00
## r_Site_ID[NAKAGUSUKUFR,TyphoonPost] 0.08 0.00
## r_Site_ID[OISTFR,TyphoonPost] -0.16 0.00
## r_Site_ID[OISTOP,TyphoonPost] -2.23 0.01
## r_Site_ID[OKUFR,TyphoonPost] -0.13 0.00
## r_Site_ID[OYAMAOP,TyphoonPost] -1.33 0.01
## r_Site_ID[SEFAFR,TyphoonPost] 0.94 0.00
## r_Site_ID[SENBARUOP,TyphoonPost] -0.62 0.00
## r_Site_ID[SUEYOSHIFR,TyphoonPost] -0.41 0.00
## r_Site_ID[TAKEYANBARUF,TyphoonPost] 0.34 0.01
## r_Site_ID[TAMAGUSUKUFR,TyphoonPost] 4.35 0.01
## r_Site_ID[TOUNANOP,TyphoonPost] -2.13 0.01
## r_Site_ID[UEHARAFR,TyphoonPost] -0.32 0.00
## r_Site_ID[YACHOOP,TyphoonPost] 0.73 0.00
## r_Site_ID[YONAFR,TyphoonPost] -0.07 0.00
## r_Site_ID[CHATANOP,LanduseDeveloped:TyphoonPost] 0.06 0.01
## r_Site_ID[GENKAOP,LanduseDeveloped:TyphoonPost] 0.81 0.01
## r_Site_ID[GESASHIOP,LanduseDeveloped:TyphoonPost] 0.00 0.01
## r_Site_ID[HEIWAOP,LanduseDeveloped:TyphoonPost] -0.02 0.01
## r_Site_ID[HENTONAOP,LanduseDeveloped:TyphoonPost] 0.60 0.00
## r_Site_ID[KATSUUDAKEFR,LanduseDeveloped:TyphoonPost] -0.10 0.00
## r_Site_ID[KEMINFR,LanduseDeveloped:TyphoonPost] 0.40 0.00
## r_Site_ID[KURASHIKIOP,LanduseDeveloped:TyphoonPost] 0.07 0.01
## r_Site_ID[MANABIFR,LanduseDeveloped:TyphoonPost] 0.10 0.00
## r_Site_ID[NAGOFR,LanduseDeveloped:TyphoonPost] -0.03 0.01
## r_Site_ID[NAKAGUSUKUFR,LanduseDeveloped:TyphoonPost] 0.00 0.01
## r_Site_ID[OISTFR,LanduseDeveloped:TyphoonPost] -0.21 0.00
## r_Site_ID[OISTOP,LanduseDeveloped:TyphoonPost] -2.33 0.01
## r_Site_ID[OKUFR,LanduseDeveloped:TyphoonPost] -0.34 0.00
## r_Site_ID[OYAMAOP,LanduseDeveloped:TyphoonPost] 0.10 0.01
## r_Site_ID[SEFAFR,LanduseDeveloped:TyphoonPost] -0.05 0.01
## r_Site_ID[SENBARUOP,LanduseDeveloped:TyphoonPost] 0.05 0.01
## r_Site_ID[SUEYOSHIFR,LanduseDeveloped:TyphoonPost] 0.05 0.01
## r_Site_ID[TAKEYANBARUF,LanduseDeveloped:TyphoonPost] 0.65 0.01
## r_Site_ID[TAMAGUSUKUFR,LanduseDeveloped:TyphoonPost] -0.32 0.01
## r_Site_ID[TOUNANOP,LanduseDeveloped:TyphoonPost] 0.15 0.01
## r_Site_ID[UEHARAFR,LanduseDeveloped:TyphoonPost] 0.01 0.01
## r_Site_ID[YACHOOP,LanduseDeveloped:TyphoonPost] -0.06 0.01
## r_Site_ID[YONAFR,LanduseDeveloped:TyphoonPost] 0.37 0.00
## lprior -18.33 0.01
## lp__ -96.42 0.16
## sd 2.5%
## b_Intercept 0.41 -1.15
## b_LanduseDeveloped 0.61 -0.96
## b_TyphoonPost 0.54 -0.67
## b_LanduseDeveloped:TyphoonPost 1.00 -2.09
## sd_Site_ID__Intercept 0.32 0.76
## sd_Site_ID__LanduseDeveloped 0.58 0.04
## sd_Site_ID__TyphoonPost 0.50 0.64
## sd_Site_ID__LanduseDeveloped:TyphoonPost 0.99 0.08
## cor_Site_ID__Intercept__LanduseDeveloped 0.42 -0.95
## cor_Site_ID__Intercept__TyphoonPost 0.21 -0.90
## cor_Site_ID__LanduseDeveloped__TyphoonPost 0.42 -0.64
## cor_Site_ID__Intercept__LanduseDeveloped:TyphoonPost 0.43 -0.78
## cor_Site_ID__LanduseDeveloped__LanduseDeveloped:TyphoonPost 0.44 -0.81
## cor_Site_ID__TyphoonPost__LanduseDeveloped:TyphoonPost 0.43 -0.77
## phi 9.55 3.37
## r_Site_ID[CHATANOP,Intercept] 0.68 -1.33
## r_Site_ID[GENKAOP,Intercept] 1.01 -2.42
## r_Site_ID[GESASHIOP,Intercept] 0.68 -0.77
## r_Site_ID[HEIWAOP,Intercept] 0.79 -3.05
## r_Site_ID[HENTONAOP,Intercept] 0.98 -2.07
## r_Site_ID[KATSUUDAKEFR,Intercept] 0.94 -1.75
## r_Site_ID[KEMINFR,Intercept] 1.11 -3.33
## r_Site_ID[KURASHIKIOP,Intercept] 0.71 -0.48
## r_Site_ID[MANABIFR,Intercept] 0.96 -1.64
## r_Site_ID[NAGOFR,Intercept] 0.68 -1.04
## r_Site_ID[NAKAGUSUKUFR,Intercept] 0.68 -1.10
## r_Site_ID[OISTFR,Intercept] 0.94 -1.86
## r_Site_ID[OISTOP,Intercept] 1.46 -2.11
## r_Site_ID[OKUFR,Intercept] 0.96 -2.04
## r_Site_ID[OYAMAOP,Intercept] 0.71 -0.65
## r_Site_ID[SEFAFR,Intercept] 0.68 -1.41
## r_Site_ID[SENBARUOP,Intercept] 0.72 -0.26
## r_Site_ID[SUEYOSHIFR,Intercept] 0.75 0.02
## r_Site_ID[TAKEYANBARUF,Intercept] 1.02 -1.98
## r_Site_ID[TAMAGUSUKUFR,Intercept] 0.98 -5.68
## r_Site_ID[TOUNANOP,Intercept] 0.73 -0.64
## r_Site_ID[UEHARAFR,Intercept] 0.69 -1.75
## r_Site_ID[YACHOOP,Intercept] 0.72 -2.25
## r_Site_ID[YONAFR,Intercept] 1.01 -1.68
## r_Site_ID[CHATANOP,LanduseDeveloped] 0.81 -1.80
## r_Site_ID[GENKAOP,LanduseDeveloped] 0.86 -1.43
## r_Site_ID[GESASHIOP,LanduseDeveloped] 0.81 -2.09
## r_Site_ID[HEIWAOP,LanduseDeveloped] 1.05 -1.26
## r_Site_ID[HENTONAOP,LanduseDeveloped] 0.83 -1.39
## r_Site_ID[KATSUUDAKEFR,LanduseDeveloped] 0.78 -1.71
## r_Site_ID[KEMINFR,LanduseDeveloped] 0.91 -2.19
## r_Site_ID[KURASHIKIOP,LanduseDeveloped] 0.85 -2.36
## r_Site_ID[MANABIFR,LanduseDeveloped] 0.80 -1.51
## r_Site_ID[NAGOFR,LanduseDeveloped] 0.81 -1.91
## r_Site_ID[NAKAGUSUKUFR,LanduseDeveloped] 0.77 -1.80
## r_Site_ID[OISTFR,LanduseDeveloped] 0.78 -1.85
## r_Site_ID[OISTOP,LanduseDeveloped] 1.36 -4.01
## r_Site_ID[OKUFR,LanduseDeveloped] 0.81 -2.04
## r_Site_ID[OYAMAOP,LanduseDeveloped] 0.85 -2.31
## r_Site_ID[SEFAFR,LanduseDeveloped] 0.81 -1.69
## r_Site_ID[SENBARUOP,LanduseDeveloped] 0.91 -2.59
## r_Site_ID[SUEYOSHIFR,LanduseDeveloped] 1.00 -2.96
## r_Site_ID[TAKEYANBARUF,LanduseDeveloped] 0.88 -1.32
## r_Site_ID[TAMAGUSUKUFR,LanduseDeveloped] 1.72 -1.12
## r_Site_ID[TOUNANOP,LanduseDeveloped] 0.93 -2.51
## r_Site_ID[UEHARAFR,LanduseDeveloped] 0.80 -1.50
## r_Site_ID[YACHOOP,LanduseDeveloped] 0.83 -1.22
## r_Site_ID[YONAFR,LanduseDeveloped] 0.85 -1.39
## r_Site_ID[CHATANOP,TyphoonPost] 0.97 -2.94
## r_Site_ID[GENKAOP,TyphoonPost] 1.32 -1.73
## r_Site_ID[GESASHIOP,TyphoonPost] 0.91 -1.70
## r_Site_ID[HEIWAOP,TyphoonPost] 0.97 -2.15
## r_Site_ID[HENTONAOP,TyphoonPost] 1.24 -2.03
## r_Site_ID[KATSUUDAKEFR,TyphoonPost] 1.15 -2.51
## r_Site_ID[KEMINFR,TyphoonPost] 1.36 -1.35
## r_Site_ID[KURASHIKIOP,TyphoonPost] 0.93 -2.58
## r_Site_ID[MANABIFR,TyphoonPost] 1.18 -2.60
## r_Site_ID[NAGOFR,TyphoonPost] 0.93 -1.23
## r_Site_ID[NAKAGUSUKUFR,TyphoonPost] 0.90 -1.68
## r_Site_ID[OISTFR,TyphoonPost] 1.16 -2.46
## r_Site_ID[OISTOP,TyphoonPost] 2.09 -6.14
## r_Site_ID[OKUFR,TyphoonPost] 1.18 -2.48
## r_Site_ID[OYAMAOP,TyphoonPost] 0.99 -3.29
## r_Site_ID[SEFAFR,TyphoonPost] 0.97 -0.89
## r_Site_ID[SENBARUOP,TyphoonPost] 0.93 -2.47
## r_Site_ID[SUEYOSHIFR,TyphoonPost] 0.94 -2.25
## r_Site_ID[TAKEYANBARUF,TyphoonPost] 1.27 -2.25
## r_Site_ID[TAMAGUSUKUFR,TyphoonPost] 1.51 0.99
## r_Site_ID[TOUNANOP,TyphoonPost] 1.13 -4.32
## r_Site_ID[UEHARAFR,TyphoonPost] 0.92 -2.15
## r_Site_ID[YACHOOP,TyphoonPost] 0.94 -1.09
## r_Site_ID[YONAFR,TyphoonPost] 1.22 -2.59
## r_Site_ID[CHATANOP,LanduseDeveloped:TyphoonPost] 1.70 -3.36
## r_Site_ID[GENKAOP,LanduseDeveloped:TyphoonPost] 1.28 -1.26
## r_Site_ID[GESASHIOP,LanduseDeveloped:TyphoonPost] 1.63 -3.57
## r_Site_ID[HEIWAOP,LanduseDeveloped:TyphoonPost] 2.01 -4.17
## r_Site_ID[HENTONAOP,LanduseDeveloped:TyphoonPost] 1.20 -1.52
## r_Site_ID[KATSUUDAKEFR,LanduseDeveloped:TyphoonPost] 1.10 -2.52
## r_Site_ID[KEMINFR,LanduseDeveloped:TyphoonPost] 1.26 -1.97
## r_Site_ID[KURASHIKIOP,LanduseDeveloped:TyphoonPost] 1.64 -3.36
## r_Site_ID[MANABIFR,LanduseDeveloped:TyphoonPost] 1.12 -2.25
## r_Site_ID[NAGOFR,LanduseDeveloped:TyphoonPost] 1.68 -3.81
## r_Site_ID[NAKAGUSUKUFR,LanduseDeveloped:TyphoonPost] 1.58 -3.46
## r_Site_ID[OISTFR,LanduseDeveloped:TyphoonPost] 1.11 -2.69
## r_Site_ID[OISTOP,LanduseDeveloped:TyphoonPost] 2.08 -7.09
## r_Site_ID[OKUFR,LanduseDeveloped:TyphoonPost] 1.13 -2.90
## r_Site_ID[OYAMAOP,LanduseDeveloped:TyphoonPost] 1.70 -3.29
## r_Site_ID[SEFAFR,LanduseDeveloped:TyphoonPost] 1.68 -3.87
## r_Site_ID[SENBARUOP,LanduseDeveloped:TyphoonPost] 1.73 -3.63
## r_Site_ID[SUEYOSHIFR,LanduseDeveloped:TyphoonPost] 1.89 -4.06
## r_Site_ID[TAKEYANBARUF,LanduseDeveloped:TyphoonPost] 1.23 -1.51
## r_Site_ID[TAMAGUSUKUFR,LanduseDeveloped:TyphoonPost] 2.97 -7.29
## r_Site_ID[TOUNANOP,LanduseDeveloped:TyphoonPost] 1.93 -3.55
## r_Site_ID[UEHARAFR,LanduseDeveloped:TyphoonPost] 1.63 -3.37
## r_Site_ID[YACHOOP,LanduseDeveloped:TyphoonPost] 1.64 -3.71
## r_Site_ID[YONAFR,LanduseDeveloped:TyphoonPost] 1.18 -1.92
## lprior 1.29 -21.18
## lp__ 16.64 -126.97
## 25% 50%
## b_Intercept -0.60 -0.33
## b_LanduseDeveloped -0.14 0.26
## b_TyphoonPost 0.05 0.40
## b_LanduseDeveloped:TyphoonPost -0.75 -0.11
## sd_Site_ID__Intercept 1.10 1.30
## sd_Site_ID__LanduseDeveloped 0.38 0.78
## sd_Site_ID__TyphoonPost 1.37 1.67
## sd_Site_ID__LanduseDeveloped:TyphoonPost 0.77 1.41
## cor_Site_ID__Intercept__LanduseDeveloped -0.77 -0.55
## cor_Site_ID__Intercept__TyphoonPost -0.76 -0.65
## cor_Site_ID__LanduseDeveloped__TyphoonPost 0.03 0.38
## cor_Site_ID__Intercept__LanduseDeveloped:TyphoonPost -0.30 0.04
## cor_Site_ID__LanduseDeveloped__LanduseDeveloped:TyphoonPost -0.34 0.00
## cor_Site_ID__TyphoonPost__LanduseDeveloped:TyphoonPost -0.33 -0.02
## phi 6.38 9.56
## r_Site_ID[CHATANOP,Intercept] -0.38 0.07
## r_Site_ID[GENKAOP,Intercept] -0.89 -0.25
## r_Site_ID[GESASHIOP,Intercept] 0.13 0.57
## r_Site_ID[HEIWAOP,Intercept] -1.93 -1.40
## r_Site_ID[HENTONAOP,Intercept] -0.60 0.02
## r_Site_ID[KATSUUDAKEFR,Intercept] -0.46 0.12
## r_Site_ID[KEMINFR,Intercept] -1.80 -1.10
## r_Site_ID[KURASHIKIOP,Intercept] 0.43 0.89
## r_Site_ID[MANABIFR,Intercept] -0.25 0.35
## r_Site_ID[NAGOFR,Intercept] -0.13 0.31
## r_Site_ID[NAKAGUSUKUFR,Intercept] -0.19 0.25
## r_Site_ID[OISTFR,Intercept] -0.59 -0.01
## r_Site_ID[OISTOP,Intercept] -0.49 0.34
## r_Site_ID[OKUFR,Intercept] -0.75 -0.17
## r_Site_ID[OYAMAOP,Intercept] 0.29 0.75
## r_Site_ID[SEFAFR,Intercept] -0.48 -0.03
## r_Site_ID[SENBARUOP,Intercept] 0.67 1.14
## r_Site_ID[SUEYOSHIFR,Intercept] 0.97 1.46
## r_Site_ID[TAKEYANBARUF,Intercept] -0.40 0.24
## r_Site_ID[TAMAGUSUKUFR,Intercept] -4.32 -3.65
## r_Site_ID[TOUNANOP,Intercept] 0.34 0.82
## r_Site_ID[UEHARAFR,Intercept] -0.77 -0.32
## r_Site_ID[YACHOOP,Intercept] -1.24 -0.77
## r_Site_ID[YONAFR,Intercept] -0.19 0.45
## r_Site_ID[CHATANOP,LanduseDeveloped] -0.40 -0.02
## r_Site_ID[GENKAOP,LanduseDeveloped] -0.16 0.11
## r_Site_ID[GESASHIOP,LanduseDeveloped] -0.54 -0.08
## r_Site_ID[HEIWAOP,LanduseDeveloped] -0.08 0.25
## r_Site_ID[HENTONAOP,LanduseDeveloped] -0.18 0.09
## r_Site_ID[KATSUUDAKEFR,LanduseDeveloped] -0.35 0.00
## r_Site_ID[KEMINFR,LanduseDeveloped] -0.41 0.00
## r_Site_ID[KURASHIKIOP,LanduseDeveloped] -0.71 -0.16
## r_Site_ID[MANABIFR,LanduseDeveloped] -0.27 0.02
## r_Site_ID[NAGOFR,LanduseDeveloped] -0.43 -0.03
## r_Site_ID[NAKAGUSUKUFR,LanduseDeveloped] -0.40 -0.03
## r_Site_ID[OISTFR,LanduseDeveloped] -0.42 -0.03
## r_Site_ID[OISTOP,LanduseDeveloped] -1.49 -0.44
## r_Site_ID[OKUFR,LanduseDeveloped] -0.48 -0.05
## r_Site_ID[OYAMAOP,LanduseDeveloped] -0.68 -0.15
## r_Site_ID[SEFAFR,LanduseDeveloped] -0.31 0.01
## r_Site_ID[SENBARUOP,LanduseDeveloped] -0.83 -0.22
## r_Site_ID[SUEYOSHIFR,LanduseDeveloped] -0.98 -0.28
## r_Site_ID[TAKEYANBARUF,LanduseDeveloped] -0.15 0.11
## r_Site_ID[TAMAGUSUKUFR,LanduseDeveloped] 0.09 0.98
## r_Site_ID[TOUNANOP,LanduseDeveloped] -0.77 -0.17
## r_Site_ID[UEHARAFR,LanduseDeveloped] -0.25 0.04
## r_Site_ID[YACHOOP,LanduseDeveloped] -0.12 0.14
## r_Site_ID[YONAFR,LanduseDeveloped] -0.20 0.07
## r_Site_ID[CHATANOP,TyphoonPost] -1.66 -1.01
## r_Site_ID[GENKAOP,TyphoonPost] 0.04 0.86
## r_Site_ID[GESASHIOP,TyphoonPost] -0.52 0.06
## r_Site_ID[HEIWAOP,TyphoonPost] -0.80 -0.17
## r_Site_ID[HENTONAOP,TyphoonPost] -0.29 0.47
## r_Site_ID[KATSUUDAKEFR,TyphoonPost] -0.87 -0.15
## r_Site_ID[KEMINFR,TyphoonPost] 0.33 1.18
## r_Site_ID[KURASHIKIOP,TyphoonPost] -1.33 -0.72
## r_Site_ID[MANABIFR,TyphoonPost] -0.92 -0.18
## r_Site_ID[NAGOFR,TyphoonPost] -0.05 0.55
## r_Site_ID[NAKAGUSUKUFR,TyphoonPost] -0.50 0.07
## r_Site_ID[OISTFR,TyphoonPost] -0.88 -0.16
## r_Site_ID[OISTOP,TyphoonPost] -3.69 -2.27
## r_Site_ID[OKUFR,TyphoonPost] -0.85 -0.13
## r_Site_ID[OYAMAOP,TyphoonPost] -1.99 -1.33
## r_Site_ID[SEFAFR,TyphoonPost] 0.28 0.92
## r_Site_ID[SENBARUOP,TyphoonPost] -1.22 -0.61
## r_Site_ID[SUEYOSHIFR,TyphoonPost] -1.02 -0.41
## r_Site_ID[TAKEYANBARUF,TyphoonPost] -0.43 0.33
## r_Site_ID[TAMAGUSUKUFR,TyphoonPost] 3.42 4.42
## r_Site_ID[TOUNANOP,TyphoonPost] -2.89 -2.15
## r_Site_ID[UEHARAFR,TyphoonPost] -0.91 -0.31
## r_Site_ID[YACHOOP,TyphoonPost] 0.11 0.71
## r_Site_ID[YONAFR,TyphoonPost] -0.81 -0.04
## r_Site_ID[CHATANOP,LanduseDeveloped:TyphoonPost] -0.71 -0.01
## r_Site_ID[GENKAOP,LanduseDeveloped:TyphoonPost] 0.00 0.57
## r_Site_ID[GESASHIOP,LanduseDeveloped:TyphoonPost] -0.68 0.01
## r_Site_ID[HEIWAOP,LanduseDeveloped:TyphoonPost] -0.96 -0.04
## r_Site_ID[HENTONAOP,LanduseDeveloped:TyphoonPost] -0.08 0.39
## r_Site_ID[KATSUUDAKEFR,LanduseDeveloped:TyphoonPost] -0.62 -0.04
## r_Site_ID[KEMINFR,LanduseDeveloped:TyphoonPost] -0.26 0.24
## r_Site_ID[KURASHIKIOP,LanduseDeveloped:TyphoonPost] -0.66 0.01
## r_Site_ID[MANABIFR,LanduseDeveloped:TyphoonPost] -0.44 0.05
## r_Site_ID[NAGOFR,LanduseDeveloped:TyphoonPost] -0.70 0.01
## r_Site_ID[NAKAGUSUKUFR,LanduseDeveloped:TyphoonPost] -0.66 0.01
## r_Site_ID[OISTFR,LanduseDeveloped:TyphoonPost] -0.74 -0.10
## r_Site_ID[OISTOP,LanduseDeveloped:TyphoonPost] -3.60 -2.05
## r_Site_ID[OKUFR,LanduseDeveloped:TyphoonPost] -0.90 -0.20
## r_Site_ID[OYAMAOP,LanduseDeveloped:TyphoonPost] -0.68 0.00
## r_Site_ID[SEFAFR,LanduseDeveloped:TyphoonPost] -0.71 0.01
## r_Site_ID[SENBARUOP,LanduseDeveloped:TyphoonPost] -0.70 0.01
## r_Site_ID[SUEYOSHIFR,LanduseDeveloped:TyphoonPost] -0.77 0.03
## r_Site_ID[TAKEYANBARUF,LanduseDeveloped:TyphoonPost] -0.06 0.44
## r_Site_ID[TAMAGUSUKUFR,LanduseDeveloped:TyphoonPost] -1.57 -0.05
## r_Site_ID[TOUNANOP,LanduseDeveloped:TyphoonPost] -0.77 0.00
## r_Site_ID[UEHARAFR,LanduseDeveloped:TyphoonPost] -0.72 -0.01
## r_Site_ID[YACHOOP,LanduseDeveloped:TyphoonPost] -0.74 -0.01
## r_Site_ID[YONAFR,LanduseDeveloped:TyphoonPost] -0.24 0.21
## lprior -19.13 -18.20
## lp__ -108.05 -97.09
## 75% 97.5% n_eff
## b_Intercept -0.06 0.49 54155
## b_LanduseDeveloped 0.66 1.46 58566
## b_TyphoonPost 0.76 1.48 59184
## b_LanduseDeveloped:TyphoonPost 0.54 1.89 58961
## sd_Site_ID__Intercept 1.52 2.01 22913
## sd_Site_ID__LanduseDeveloped 1.24 2.16 33241
## sd_Site_ID__TyphoonPost 1.99 2.68 20343
## sd_Site_ID__LanduseDeveloped:TyphoonPost 2.14 3.76 34691
## cor_Site_ID__Intercept__LanduseDeveloped -0.18 0.59 48484
## cor_Site_ID__Intercept__TyphoonPost -0.51 -0.12 53106
## cor_Site_ID__LanduseDeveloped__TyphoonPost 0.63 0.90 27642
## cor_Site_ID__Intercept__LanduseDeveloped:TyphoonPost 0.37 0.81 65966
## cor_Site_ID__LanduseDeveloped__LanduseDeveloped:TyphoonPost 0.34 0.81 57377
## cor_Site_ID__TyphoonPost__LanduseDeveloped:TyphoonPost 0.32 0.80 56619
## phi 14.89 37.59 11060
## r_Site_ID[CHATANOP,Intercept] 0.51 1.38 63838
## r_Site_ID[GENKAOP,Intercept] 0.35 1.64 54616
## r_Site_ID[GESASHIOP,Intercept] 1.01 1.94 57360
## r_Site_ID[HEIWAOP,Intercept] -0.89 0.06 41409
## r_Site_ID[HENTONAOP,Intercept] 0.61 1.88 61553
## r_Site_ID[KATSUUDAKEFR,Intercept] 0.69 2.01 70243
## r_Site_ID[KEMINFR,Intercept] -0.41 1.13 42358
## r_Site_ID[KURASHIKIOP,Intercept] 1.35 2.30 49714
## r_Site_ID[MANABIFR,Intercept] 0.94 2.26 67449
## r_Site_ID[NAGOFR,Intercept] 0.75 1.65 65896
## r_Site_ID[NAKAGUSUKUFR,Intercept] 0.68 1.57 65298
## r_Site_ID[OISTFR,Intercept] 0.57 1.90 71232
## r_Site_ID[OISTOP,Intercept] 1.33 3.72 33054
## r_Site_ID[OKUFR,Intercept] 0.44 1.81 68325
## r_Site_ID[OYAMAOP,Intercept] 1.21 2.15 45017
## r_Site_ID[SEFAFR,Intercept] 0.40 1.30 65954
## r_Site_ID[SENBARUOP,Intercept] 1.61 2.60 44385
## r_Site_ID[SUEYOSHIFR,Intercept] 1.96 2.99 39043
## r_Site_ID[TAKEYANBARUF,Intercept] 0.85 2.15 52647
## r_Site_ID[TAMAGUSUKUFR,Intercept] -3.01 -1.84 19731
## r_Site_ID[TOUNANOP,Intercept] 1.29 2.22 43669
## r_Site_ID[UEHARAFR,Intercept] 0.12 0.98 63796
## r_Site_ID[YACHOOP,Intercept] -0.30 0.60 50561
## r_Site_ID[YONAFR,Intercept] 1.07 2.40 61599
## r_Site_ID[CHATANOP,LanduseDeveloped] 0.30 1.70 57995
## r_Site_ID[GENKAOP,LanduseDeveloped] 0.64 2.27 51949
## r_Site_ID[GESASHIOP,LanduseDeveloped] 0.18 1.37 63828
## r_Site_ID[HEIWAOP,LanduseDeveloped] 0.97 3.05 46050
## r_Site_ID[HENTONAOP,LanduseDeveloped] 0.57 2.19 57560
## r_Site_ID[KATSUUDAKEFR,LanduseDeveloped] 0.32 1.68 68597
## r_Site_ID[KEMINFR,LanduseDeveloped] 0.38 1.76 56474
## r_Site_ID[KURASHIKIOP,LanduseDeveloped] 0.10 1.20 57232
## r_Site_ID[MANABIFR,LanduseDeveloped] 0.42 1.97 69297
## r_Site_ID[NAGOFR,LanduseDeveloped] 0.27 1.55 60426
## r_Site_ID[NAKAGUSUKUFR,LanduseDeveloped] 0.25 1.48 67553
## r_Site_ID[OISTFR,LanduseDeveloped] 0.26 1.55 66456
## r_Site_ID[OISTOP,LanduseDeveloped] 0.05 1.45 31089
## r_Site_ID[OKUFR,LanduseDeveloped] 0.22 1.44 65773
## r_Site_ID[OYAMAOP,LanduseDeveloped] 0.12 1.28 55727
## r_Site_ID[SEFAFR,LanduseDeveloped] 0.39 1.79 56545
## r_Site_ID[SENBARUOP,LanduseDeveloped] 0.07 1.12 52192
## r_Site_ID[SUEYOSHIFR,LanduseDeveloped] 0.05 1.14 45396
## r_Site_ID[TAKEYANBARUF,LanduseDeveloped] 0.65 2.41 48020
## r_Site_ID[TAMAGUSUKUFR,LanduseDeveloped] 2.40 5.40 34013
## r_Site_ID[TOUNANOP,LanduseDeveloped] 0.13 1.40 45071
## r_Site_ID[UEHARAFR,LanduseDeveloped] 0.44 1.92 67879
## r_Site_ID[YACHOOP,LanduseDeveloped] 0.65 2.26 63570
## r_Site_ID[YONAFR,LanduseDeveloped] 0.57 2.27 59861
## r_Site_ID[CHATANOP,TyphoonPost] -0.37 0.85 47673
## r_Site_ID[GENKAOP,TyphoonPost] 1.73 3.49 46609
## r_Site_ID[GESASHIOP,TyphoonPost] 0.65 1.90 69768
## r_Site_ID[HEIWAOP,TyphoonPost] 0.45 1.72 67412
## r_Site_ID[HENTONAOP,TyphoonPost] 1.26 2.95 57122
## r_Site_ID[KATSUUDAKEFR,TyphoonPost] 0.54 2.12 71899
## r_Site_ID[KEMINFR,TyphoonPost] 2.08 3.99 43674
## r_Site_ID[KURASHIKIOP,TyphoonPost] -0.11 1.08 58512
## r_Site_ID[MANABIFR,TyphoonPost] 0.52 2.14 70602
## r_Site_ID[NAGOFR,TyphoonPost] 1.17 2.45 58720
## r_Site_ID[NAKAGUSUKUFR,TyphoonPost] 0.65 1.89 69166
## r_Site_ID[OISTFR,TyphoonPost] 0.54 2.18 69247
## r_Site_ID[OISTOP,TyphoonPost] -0.78 1.91 28369
## r_Site_ID[OKUFR,TyphoonPost] 0.59 2.26 68211
## r_Site_ID[OYAMAOP,TyphoonPost] -0.66 0.56 39406
## r_Site_ID[SEFAFR,TyphoonPost] 1.57 2.91 52974
## r_Site_ID[SENBARUOP,TyphoonPost] -0.02 1.22 59681
## r_Site_ID[SUEYOSHIFR,TyphoonPost] 0.19 1.48 67799
## r_Site_ID[TAKEYANBARUF,TyphoonPost] 1.14 2.85 54815
## r_Site_ID[TAMAGUSUKUFR,TyphoonPost] 5.37 7.11 19068
## r_Site_ID[TOUNANOP,TyphoonPost] -1.36 0.08 28983
## r_Site_ID[UEHARAFR,TyphoonPost] 0.27 1.49 65060
## r_Site_ID[YACHOOP,TyphoonPost] 1.35 2.62 55594
## r_Site_ID[YONAFR,TyphoonPost] 0.70 2.32 68134
## r_Site_ID[CHATANOP,LanduseDeveloped:TyphoonPost] 0.72 3.94 61966
## r_Site_ID[GENKAOP,LanduseDeveloped:TyphoonPost] 1.47 3.88 54023
## r_Site_ID[GESASHIOP,LanduseDeveloped:TyphoonPost] 0.74 3.42 71614
## r_Site_ID[HEIWAOP,LanduseDeveloped:TyphoonPost] 0.82 4.46 62883
## r_Site_ID[HENTONAOP,LanduseDeveloped:TyphoonPost] 1.21 3.45 58567
## r_Site_ID[KATSUUDAKEFR,LanduseDeveloped:TyphoonPost] 0.44 2.19 69958
## r_Site_ID[KEMINFR,LanduseDeveloped:TyphoonPost] 1.02 3.28 64725
## r_Site_ID[KURASHIKIOP,LanduseDeveloped:TyphoonPost] 0.76 3.69 68292
## r_Site_ID[MANABIFR,LanduseDeveloped:TyphoonPost] 0.65 2.53 70809
## r_Site_ID[NAGOFR,LanduseDeveloped:TyphoonPost] 0.74 3.39 61797
## r_Site_ID[NAKAGUSUKUFR,LanduseDeveloped:TyphoonPost] 0.68 3.33 71099
## r_Site_ID[OISTFR,LanduseDeveloped:TyphoonPost] 0.34 1.99 65934
## r_Site_ID[OISTOP,LanduseDeveloped:TyphoonPost] -0.67 0.61 34501
## r_Site_ID[OKUFR,LanduseDeveloped:TyphoonPost] 0.24 1.86 66713
## r_Site_ID[OYAMAOP,LanduseDeveloped:TyphoonPost] 0.77 4.00 65789
## r_Site_ID[SEFAFR,LanduseDeveloped:TyphoonPost] 0.72 3.32 62250
## r_Site_ID[SENBARUOP,LanduseDeveloped:TyphoonPost] 0.81 3.76 70028
## r_Site_ID[SUEYOSHIFR,LanduseDeveloped:TyphoonPost] 0.91 4.06 68575
## r_Site_ID[TAKEYANBARUF,LanduseDeveloped:TyphoonPost] 1.29 3.56 56924
## r_Site_ID[TAMAGUSUKUFR,LanduseDeveloped:TyphoonPost] 1.13 5.39 53166
## r_Site_ID[TOUNANOP,LanduseDeveloped:TyphoonPost] 0.88 4.73 54240
## r_Site_ID[UEHARAFR,LanduseDeveloped:TyphoonPost] 0.68 3.64 69825
## r_Site_ID[YACHOOP,LanduseDeveloped:TyphoonPost] 0.65 3.38 70416
## r_Site_ID[YONAFR,LanduseDeveloped:TyphoonPost] 0.95 3.07 67368
## lprior -17.41 -16.18 17903
## lp__ -85.45 -61.98 11404
## Rhat
## b_Intercept 1
## b_LanduseDeveloped 1
## b_TyphoonPost 1
## b_LanduseDeveloped:TyphoonPost 1
## sd_Site_ID__Intercept 1
## sd_Site_ID__LanduseDeveloped 1
## sd_Site_ID__TyphoonPost 1
## sd_Site_ID__LanduseDeveloped:TyphoonPost 1
## cor_Site_ID__Intercept__LanduseDeveloped 1
## cor_Site_ID__Intercept__TyphoonPost 1
## cor_Site_ID__LanduseDeveloped__TyphoonPost 1
## cor_Site_ID__Intercept__LanduseDeveloped:TyphoonPost 1
## cor_Site_ID__LanduseDeveloped__LanduseDeveloped:TyphoonPost 1
## cor_Site_ID__TyphoonPost__LanduseDeveloped:TyphoonPost 1
## phi 1
## r_Site_ID[CHATANOP,Intercept] 1
## r_Site_ID[GENKAOP,Intercept] 1
## r_Site_ID[GESASHIOP,Intercept] 1
## r_Site_ID[HEIWAOP,Intercept] 1
## r_Site_ID[HENTONAOP,Intercept] 1
## r_Site_ID[KATSUUDAKEFR,Intercept] 1
## r_Site_ID[KEMINFR,Intercept] 1
## r_Site_ID[KURASHIKIOP,Intercept] 1
## r_Site_ID[MANABIFR,Intercept] 1
## r_Site_ID[NAGOFR,Intercept] 1
## r_Site_ID[NAKAGUSUKUFR,Intercept] 1
## r_Site_ID[OISTFR,Intercept] 1
## r_Site_ID[OISTOP,Intercept] 1
## r_Site_ID[OKUFR,Intercept] 1
## r_Site_ID[OYAMAOP,Intercept] 1
## r_Site_ID[SEFAFR,Intercept] 1
## r_Site_ID[SENBARUOP,Intercept] 1
## r_Site_ID[SUEYOSHIFR,Intercept] 1
## r_Site_ID[TAKEYANBARUF,Intercept] 1
## r_Site_ID[TAMAGUSUKUFR,Intercept] 1
## r_Site_ID[TOUNANOP,Intercept] 1
## r_Site_ID[UEHARAFR,Intercept] 1
## r_Site_ID[YACHOOP,Intercept] 1
## r_Site_ID[YONAFR,Intercept] 1
## r_Site_ID[CHATANOP,LanduseDeveloped] 1
## r_Site_ID[GENKAOP,LanduseDeveloped] 1
## r_Site_ID[GESASHIOP,LanduseDeveloped] 1
## r_Site_ID[HEIWAOP,LanduseDeveloped] 1
## r_Site_ID[HENTONAOP,LanduseDeveloped] 1
## r_Site_ID[KATSUUDAKEFR,LanduseDeveloped] 1
## r_Site_ID[KEMINFR,LanduseDeveloped] 1
## r_Site_ID[KURASHIKIOP,LanduseDeveloped] 1
## r_Site_ID[MANABIFR,LanduseDeveloped] 1
## r_Site_ID[NAGOFR,LanduseDeveloped] 1
## r_Site_ID[NAKAGUSUKUFR,LanduseDeveloped] 1
## r_Site_ID[OISTFR,LanduseDeveloped] 1
## r_Site_ID[OISTOP,LanduseDeveloped] 1
## r_Site_ID[OKUFR,LanduseDeveloped] 1
## r_Site_ID[OYAMAOP,LanduseDeveloped] 1
## r_Site_ID[SEFAFR,LanduseDeveloped] 1
## r_Site_ID[SENBARUOP,LanduseDeveloped] 1
## r_Site_ID[SUEYOSHIFR,LanduseDeveloped] 1
## r_Site_ID[TAKEYANBARUF,LanduseDeveloped] 1
## r_Site_ID[TAMAGUSUKUFR,LanduseDeveloped] 1
## r_Site_ID[TOUNANOP,LanduseDeveloped] 1
## r_Site_ID[UEHARAFR,LanduseDeveloped] 1
## r_Site_ID[YACHOOP,LanduseDeveloped] 1
## r_Site_ID[YONAFR,LanduseDeveloped] 1
## r_Site_ID[CHATANOP,TyphoonPost] 1
## r_Site_ID[GENKAOP,TyphoonPost] 1
## r_Site_ID[GESASHIOP,TyphoonPost] 1
## r_Site_ID[HEIWAOP,TyphoonPost] 1
## r_Site_ID[HENTONAOP,TyphoonPost] 1
## r_Site_ID[KATSUUDAKEFR,TyphoonPost] 1
## r_Site_ID[KEMINFR,TyphoonPost] 1
## r_Site_ID[KURASHIKIOP,TyphoonPost] 1
## r_Site_ID[MANABIFR,TyphoonPost] 1
## r_Site_ID[NAGOFR,TyphoonPost] 1
## r_Site_ID[NAKAGUSUKUFR,TyphoonPost] 1
## r_Site_ID[OISTFR,TyphoonPost] 1
## r_Site_ID[OISTOP,TyphoonPost] 1
## r_Site_ID[OKUFR,TyphoonPost] 1
## r_Site_ID[OYAMAOP,TyphoonPost] 1
## r_Site_ID[SEFAFR,TyphoonPost] 1
## r_Site_ID[SENBARUOP,TyphoonPost] 1
## r_Site_ID[SUEYOSHIFR,TyphoonPost] 1
## r_Site_ID[TAKEYANBARUF,TyphoonPost] 1
## r_Site_ID[TAMAGUSUKUFR,TyphoonPost] 1
## r_Site_ID[TOUNANOP,TyphoonPost] 1
## r_Site_ID[UEHARAFR,TyphoonPost] 1
## r_Site_ID[YACHOOP,TyphoonPost] 1
## r_Site_ID[YONAFR,TyphoonPost] 1
## r_Site_ID[CHATANOP,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[GENKAOP,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[GESASHIOP,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[HEIWAOP,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[HENTONAOP,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[KATSUUDAKEFR,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[KEMINFR,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[KURASHIKIOP,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[MANABIFR,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[NAGOFR,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[NAKAGUSUKUFR,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[OISTFR,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[OISTOP,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[OKUFR,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[OYAMAOP,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[SEFAFR,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[SENBARUOP,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[SUEYOSHIFR,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[TAKEYANBARUF,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[TAMAGUSUKUFR,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[TOUNANOP,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[UEHARAFR,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[YACHOOP,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[YONAFR,LanduseDeveloped:TyphoonPost] 1
## lprior 1
## lp__ 1
##
## Samples were drawn using NUTS(diag_e) at Mon Nov 14 13:33:58 2022.
## For each parameter, n_eff is a crude measure of effective sample size,
## and Rhat is the potential scale reduction factor on split chains (at
## convergence, Rhat=1).bayes_R2(mod_nonspatial_beta) # pseudo r-squared of model## Estimate Est.Error Q2.5 Q97.5
## R2 0.7343529 0.1259734 0.4589802 0.9300482# simulate data from 500 random draws of posterior and compare it to observed data
# the black line should run through the center of the blue lines
pp_check(mod_nonspatial_beta, ndraws = 500) + theme_cowplot()
## Spatial Autocorrelation test
bres <- residuals(mod_nonspatial_beta)[,"Estimate"]
d_mat = as.matrix(dist(df[,c("Lat", "Long")], diag=T, upper=T)) # make distance matrix
d_mat_inv <- 1/d_mat
d_mat_inv[which(d_mat_inv == Inf)] <- 0
Moran.I(bres, d_mat_inv) # non-significant = no spatial autocorrelation## $observed
## [1] -0.06653623
##
## $expected
## [1] -0.0212766
##
## $sd
## [1] 0.04103721
##
## $p.value
## [1] 0.2700738NDSI_Anthro - no effect of typhoon or land use
# get pre vs post data for comparison
dat_pre<-tidy.stability_AI %>%
filter(Index %in% "NDSI_Anth" & response_variable %in% 'Pre_Var')
dat_post<-tidy.stability_AI %>%
filter(Index %in% "NDSI_Anth" & response_variable %in% 'Post_Var')
dat_pre<-dat_pre[complete.cases(dat_pre),]
dat_post<-dat_post[complete.cases(dat_post),]
dat_pre$Typhoon = rep("Pre", nrow(dat_pre))
dat_post$Typhoon = rep("Post", nrow(dat_post))
df <- rbind(dat_pre, dat_post)
#names(df)[1] <- names(Landuse_1000)[1]
#df <- left_join(df, Landuse_1000, by = "site_id")
# change levels for model
df$Landuse<-df$Landuse %>% parse_character() %>% parse_factor(levels = c('Forest','Developed'))
df$Typhoon<-df$Typhoon %>% parse_character() %>% parse_factor(levels = c('Pre','Post'))
#df$site_id<-df$site_id %>% parse_character() %>% parse_factor(levels = c(df$site_id[order(df$PC1)]))
# fit random intercept and slope model because random slope model didn't converge well.
mod_nonspatial_beta <-
brm(data = df, family = Beta(),
Stability ~ 1 + Landuse * Typhoon + (1+Landuse*Typhoon|Site_ID),
iter = 5e4, warmup = 5000, chains = 4, cores = 4, thin = 2,
seed = 666)## Compiling Stan program...## Start sampling## Warning: There were 17 divergent transitions after warmup. See
## https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## to find out why this is a problem and how to eliminate them.## Warning: Examine the pairs() plot to diagnose sampling problemsmod_nonspatial_beta <- add_criterion(mod_nonspatial_beta, "loo")## Warning: Found 38 observations with a pareto_k > 0.7 in model
## 'mod_nonspatial_beta'. It is recommended to set 'moment_match = TRUE' in order
## to perform moment matching for problematic observations.#check MCMC traces
mcmc_plot(mod_nonspatial_beta, type = "trace") + theme_cowplot()
#check for agreement of chains
mcmc_plot(mod_nonspatial_beta, type = "dens_overlay") + theme_cowplot()
# plot posterior estimates of fixed effects
mcmc_plot(mod_nonspatial_beta, type = "intervals",prob = 0.68, prob_outer = 0.95, variable = "^b_", regex = TRUE) + theme_cowplot()
## no effects ##
## Parameter fits and stats
mod_nonspatial_beta$fit # Rhat values closer to 1 and n_eff values > 1000 are ideal.## Inference for Stan model: 1b3a3d37e3bf884746f549577180da75.
## 4 chains, each with iter=50000; warmup=5000; thin=2;
## post-warmup draws per chain=22500, total post-warmup draws=90000.
##
## mean se_mean
## b_Intercept 0.48 0.00
## b_LanduseDeveloped -0.98 0.00
## b_TyphoonPost -0.73 0.00
## b_LanduseDeveloped:TyphoonPost 0.34 0.01
## sd_Site_ID__Intercept 0.85 0.00
## sd_Site_ID__LanduseDeveloped 1.84 0.00
## sd_Site_ID__TyphoonPost 1.42 0.00
## sd_Site_ID__LanduseDeveloped:TyphoonPost 1.89 0.01
## cor_Site_ID__Intercept__LanduseDeveloped -0.09 0.00
## cor_Site_ID__Intercept__TyphoonPost 0.50 0.00
## cor_Site_ID__LanduseDeveloped__TyphoonPost -0.22 0.00
## cor_Site_ID__Intercept__LanduseDeveloped:TyphoonPost -0.12 0.00
## cor_Site_ID__LanduseDeveloped__LanduseDeveloped:TyphoonPost -0.29 0.00
## cor_Site_ID__TyphoonPost__LanduseDeveloped:TyphoonPost -0.04 0.00
## phi 21.23 0.25
## r_Site_ID[CHATANOP,Intercept] 0.64 0.00
## r_Site_ID[GENKAOP,Intercept] 0.59 0.00
## r_Site_ID[GESASHIOP,Intercept] -1.44 0.00
## r_Site_ID[HEIWAOP,Intercept] 1.21 0.00
## r_Site_ID[HENTONAOP,Intercept] -0.16 0.00
## r_Site_ID[KATSUUDAKEFR,Intercept] -0.05 0.00
## r_Site_ID[KEMINFR,Intercept] -0.12 0.00
## r_Site_ID[KURASHIKIOP,Intercept] -0.48 0.00
## r_Site_ID[MANABIFR,Intercept] -0.38 0.01
## r_Site_ID[NAGOFR,Intercept] -0.59 0.00
## r_Site_ID[NAKAGUSUKUFR,Intercept] -0.44 0.00
## r_Site_ID[OISTFR,Intercept] -0.04 0.00
## r_Site_ID[OISTOP,Intercept] 0.22 0.00
## r_Site_ID[OKUFR,Intercept] -0.04 0.00
## r_Site_ID[OYAMAOP,Intercept] 0.38 0.00
## r_Site_ID[SEFAFR,Intercept] -0.76 0.00
## r_Site_ID[SENBARUOP,Intercept] 0.28 0.00
## r_Site_ID[SUEYOSHIFR,Intercept] 0.15 0.00
## r_Site_ID[TAKEYANBARUF,Intercept] 0.31 0.00
## r_Site_ID[TAMAGUSUKUFR,Intercept] -0.28 0.00
## r_Site_ID[TOUNANOP,Intercept] 0.16 0.00
## r_Site_ID[UEHARAFR,Intercept] 0.72 0.00
## r_Site_ID[YACHOOP,Intercept] 0.44 0.00
## r_Site_ID[YONAFR,Intercept] -0.31 0.01
## r_Site_ID[CHATANOP,LanduseDeveloped] -0.34 0.01
## r_Site_ID[GENKAOP,LanduseDeveloped] 1.36 0.01
## r_Site_ID[GESASHIOP,LanduseDeveloped] 1.14 0.01
## r_Site_ID[HEIWAOP,LanduseDeveloped] -0.49 0.01
## r_Site_ID[HENTONAOP,LanduseDeveloped] 0.09 0.00
## r_Site_ID[KATSUUDAKEFR,LanduseDeveloped] 0.22 0.00
## r_Site_ID[KEMINFR,LanduseDeveloped] 0.63 0.00
## r_Site_ID[KURASHIKIOP,LanduseDeveloped] 0.16 0.01
## r_Site_ID[MANABIFR,LanduseDeveloped] -3.98 0.01
## r_Site_ID[NAGOFR,LanduseDeveloped] 0.03 0.01
## r_Site_ID[NAKAGUSUKUFR,LanduseDeveloped] 0.14 0.01
## r_Site_ID[OISTFR,LanduseDeveloped] 0.24 0.01
## r_Site_ID[OISTOP,LanduseDeveloped] 0.91 0.00
## r_Site_ID[OKUFR,LanduseDeveloped] 0.13 0.00
## r_Site_ID[OYAMAOP,LanduseDeveloped] -0.03 0.01
## r_Site_ID[SEFAFR,LanduseDeveloped] 0.48 0.01
## r_Site_ID[SENBARUOP,LanduseDeveloped] -0.27 0.01
## r_Site_ID[SUEYOSHIFR,LanduseDeveloped] -0.06 0.01
## r_Site_ID[TAKEYANBARUF,LanduseDeveloped] 0.91 0.00
## r_Site_ID[TAMAGUSUKUFR,LanduseDeveloped] 0.08 0.01
## r_Site_ID[TOUNANOP,LanduseDeveloped] 0.02 0.01
## r_Site_ID[UEHARAFR,LanduseDeveloped] -0.40 0.01
## r_Site_ID[YACHOOP,LanduseDeveloped] -0.46 0.01
## r_Site_ID[YONAFR,LanduseDeveloped] -0.59 0.01
## r_Site_ID[CHATANOP,TyphoonPost] 0.91 0.00
## r_Site_ID[GENKAOP,TyphoonPost] 0.93 0.01
## r_Site_ID[GESASHIOP,TyphoonPost] -3.27 0.01
## r_Site_ID[HEIWAOP,TyphoonPost] 1.25 0.00
## r_Site_ID[HENTONAOP,TyphoonPost] -0.55 0.01
## r_Site_ID[KATSUUDAKEFR,TyphoonPost] -0.30 0.01
## r_Site_ID[KEMINFR,TyphoonPost] -0.78 0.01
## r_Site_ID[KURASHIKIOP,TyphoonPost] -0.40 0.00
## r_Site_ID[MANABIFR,TyphoonPost] 1.39 0.01
## r_Site_ID[NAGOFR,TyphoonPost] 0.12 0.00
## r_Site_ID[NAKAGUSUKUFR,TyphoonPost] -0.31 0.00
## r_Site_ID[OISTFR,TyphoonPost] -0.30 0.01
## r_Site_ID[OISTOP,TyphoonPost] 0.06 0.00
## r_Site_ID[OKUFR,TyphoonPost] -0.23 0.01
## r_Site_ID[OYAMAOP,TyphoonPost] -0.01 0.00
## r_Site_ID[SEFAFR,TyphoonPost] -1.34 0.00
## r_Site_ID[SENBARUOP,TyphoonPost] 0.78 0.00
## r_Site_ID[SUEYOSHIFR,TyphoonPost] 0.14 0.00
## r_Site_ID[TAKEYANBARUF,TyphoonPost] 0.37 0.01
## r_Site_ID[TAMAGUSUKUFR,TyphoonPost] -0.18 0.00
## r_Site_ID[TOUNANOP,TyphoonPost] -0.12 0.00
## r_Site_ID[UEHARAFR,TyphoonPost] 1.08 0.00
## r_Site_ID[YACHOOP,TyphoonPost] 1.35 0.00
## r_Site_ID[YONAFR,TyphoonPost] -0.59 0.01
## r_Site_ID[CHATANOP,LanduseDeveloped:TyphoonPost] -0.31 0.02
## r_Site_ID[GENKAOP,LanduseDeveloped:TyphoonPost] 0.92 0.01
## r_Site_ID[GESASHIOP,LanduseDeveloped:TyphoonPost] 0.79 0.03
## r_Site_ID[HEIWAOP,LanduseDeveloped:TyphoonPost] -0.53 0.01
## r_Site_ID[HENTONAOP,LanduseDeveloped:TyphoonPost] -0.86 0.01
## r_Site_ID[KATSUUDAKEFR,LanduseDeveloped:TyphoonPost] -0.52 0.01
## r_Site_ID[KEMINFR,LanduseDeveloped:TyphoonPost] -1.39 0.01
## r_Site_ID[KURASHIKIOP,LanduseDeveloped:TyphoonPost] 0.21 0.01
## r_Site_ID[MANABIFR,LanduseDeveloped:TyphoonPost] 3.44 0.02
## r_Site_ID[NAGOFR,LanduseDeveloped:TyphoonPost] 0.17 0.02
## r_Site_ID[NAKAGUSUKUFR,LanduseDeveloped:TyphoonPost] 0.17 0.01
## r_Site_ID[OISTFR,LanduseDeveloped:TyphoonPost] -0.55 0.01
## r_Site_ID[OISTOP,LanduseDeveloped:TyphoonPost] -0.25 0.01
## r_Site_ID[OKUFR,LanduseDeveloped:TyphoonPost] -0.38 0.01
## r_Site_ID[OYAMAOP,LanduseDeveloped:TyphoonPost] -0.12 0.01
## r_Site_ID[SEFAFR,LanduseDeveloped:TyphoonPost] 0.38 0.01
## r_Site_ID[SENBARUOP,LanduseDeveloped:TyphoonPost] -0.16 0.01
## r_Site_ID[SUEYOSHIFR,LanduseDeveloped:TyphoonPost] -0.08 0.01
## r_Site_ID[TAKEYANBARUF,LanduseDeveloped:TyphoonPost] 0.20 0.01
## r_Site_ID[TAMAGUSUKUFR,LanduseDeveloped:TyphoonPost] 0.11 0.01
## r_Site_ID[TOUNANOP,LanduseDeveloped:TyphoonPost] -0.05 0.01
## r_Site_ID[UEHARAFR,LanduseDeveloped:TyphoonPost] -0.35 0.01
## r_Site_ID[YACHOOP,LanduseDeveloped:TyphoonPost] -0.26 0.02
## r_Site_ID[YONAFR,LanduseDeveloped:TyphoonPost] -0.70 0.02
## lprior -18.81 0.01
## lp__ -77.61 0.18
## sd 2.5%
## b_Intercept 0.28 -0.07
## b_LanduseDeveloped 0.72 -2.42
## b_TyphoonPost 0.45 -1.62
## b_LanduseDeveloped:TyphoonPost 0.95 -1.55
## sd_Site_ID__Intercept 0.29 0.37
## sd_Site_ID__LanduseDeveloped 0.74 0.41
## sd_Site_ID__TyphoonPost 0.44 0.65
## sd_Site_ID__LanduseDeveloped:TyphoonPost 1.06 0.14
## cor_Site_ID__Intercept__LanduseDeveloped 0.43 -0.83
## cor_Site_ID__Intercept__TyphoonPost 0.29 -0.16
## cor_Site_ID__LanduseDeveloped__TyphoonPost 0.42 -0.86
## cor_Site_ID__Intercept__LanduseDeveloped:TyphoonPost 0.44 -0.85
## cor_Site_ID__LanduseDeveloped__LanduseDeveloped:TyphoonPost 0.38 -0.88
## cor_Site_ID__TyphoonPost__LanduseDeveloped:TyphoonPost 0.43 -0.82
## phi 14.38 6.11
## r_Site_ID[CHATANOP,Intercept] 0.45 -0.19
## r_Site_ID[GENKAOP,Intercept] 0.92 -1.26
## r_Site_ID[GESASHIOP,Intercept] 0.59 -2.74
## r_Site_ID[HEIWAOP,Intercept] 0.54 0.22
## r_Site_ID[HENTONAOP,Intercept] 0.71 -1.59
## r_Site_ID[KATSUUDAKEFR,Intercept] 0.68 -1.42
## r_Site_ID[KEMINFR,Intercept] 0.76 -1.62
## r_Site_ID[KURASHIKIOP,Intercept] 0.43 -1.37
## r_Site_ID[MANABIFR,Intercept] 1.48 -3.61
## r_Site_ID[NAGOFR,Intercept] 0.46 -1.53
## r_Site_ID[NAKAGUSUKUFR,Intercept] 0.43 -1.31
## r_Site_ID[OISTFR,Intercept] 0.68 -1.43
## r_Site_ID[OISTOP,Intercept] 0.72 -1.21
## r_Site_ID[OKUFR,Intercept] 0.67 -1.39
## r_Site_ID[OYAMAOP,Intercept] 0.44 -0.43
## r_Site_ID[SEFAFR,Intercept] 0.45 -1.69
## r_Site_ID[SENBARUOP,Intercept] 0.43 -0.54
## r_Site_ID[SUEYOSHIFR,Intercept] 0.41 -0.66
## r_Site_ID[TAKEYANBARUF,Intercept] 0.75 -1.20
## r_Site_ID[TAMAGUSUKUFR,Intercept] 0.42 -1.13
## r_Site_ID[TOUNANOP,Intercept] 0.42 -0.65
## r_Site_ID[UEHARAFR,Intercept] 0.46 -0.12
## r_Site_ID[YACHOOP,Intercept] 0.45 -0.42
## r_Site_ID[YONAFR,Intercept] 0.76 -1.85
## r_Site_ID[CHATANOP,LanduseDeveloped] 1.84 -4.31
## r_Site_ID[GENKAOP,LanduseDeveloped] 1.21 -0.78
## r_Site_ID[GESASHIOP,LanduseDeveloped] 2.75 -3.99
## r_Site_ID[HEIWAOP,LanduseDeveloped] 2.17 -5.23
## r_Site_ID[HENTONAOP,LanduseDeveloped] 1.00 -1.95
## r_Site_ID[KATSUUDAKEFR,LanduseDeveloped] 0.98 -1.74
## r_Site_ID[KEMINFR,LanduseDeveloped] 1.04 -1.37
## r_Site_ID[KURASHIKIOP,LanduseDeveloped] 1.77 -3.32
## r_Site_ID[MANABIFR,LanduseDeveloped] 1.83 -7.66
## r_Site_ID[NAGOFR,LanduseDeveloped] 1.88 -3.67
## r_Site_ID[NAKAGUSUKUFR,LanduseDeveloped] 1.76 -3.37
## r_Site_ID[OISTFR,LanduseDeveloped] 0.98 -1.74
## r_Site_ID[OISTOP,LanduseDeveloped] 1.01 -0.93
## r_Site_ID[OKUFR,LanduseDeveloped] 0.97 -1.83
## r_Site_ID[OYAMAOP,LanduseDeveloped] 1.75 -3.75
## r_Site_ID[SEFAFR,LanduseDeveloped] 1.95 -3.25
## r_Site_ID[SENBARUOP,LanduseDeveloped] 1.75 -3.97
## r_Site_ID[SUEYOSHIFR,LanduseDeveloped] 1.68 -3.56
## r_Site_ID[TAKEYANBARUF,LanduseDeveloped] 1.03 -0.98
## r_Site_ID[TAMAGUSUKUFR,LanduseDeveloped] 1.70 -3.33
## r_Site_ID[TOUNANOP,LanduseDeveloped] 1.69 -3.52
## r_Site_ID[UEHARAFR,LanduseDeveloped] 1.88 -4.44
## r_Site_ID[YACHOOP,LanduseDeveloped] 1.89 -4.44
## r_Site_ID[YONAFR,LanduseDeveloped] 1.07 -2.91
## r_Site_ID[CHATANOP,TyphoonPost] 0.70 -0.39
## r_Site_ID[GENKAOP,TyphoonPost] 1.40 -1.96
## r_Site_ID[GESASHIOP,TyphoonPost] 1.06 -5.40
## r_Site_ID[HEIWAOP,TyphoonPost] 0.81 -0.24
## r_Site_ID[HENTONAOP,TyphoonPost] 1.15 -2.88
## r_Site_ID[KATSUUDAKEFR,TyphoonPost] 1.06 -2.45
## r_Site_ID[KEMINFR,TyphoonPost] 1.28 -3.37
## r_Site_ID[KURASHIKIOP,TyphoonPost] 0.68 -1.80
## r_Site_ID[MANABIFR,TyphoonPost] 2.30 -3.05
## r_Site_ID[NAGOFR,TyphoonPost] 0.70 -1.29
## r_Site_ID[NAKAGUSUKUFR,TyphoonPost] 0.67 -1.69
## r_Site_ID[OISTFR,TyphoonPost] 1.08 -2.48
## r_Site_ID[OISTOP,TyphoonPost] 1.07 -2.09
## r_Site_ID[OKUFR,TyphoonPost] 1.05 -2.35
## r_Site_ID[OYAMAOP,TyphoonPost] 0.65 -1.32
## r_Site_ID[SEFAFR,TyphoonPost] 0.78 -2.99
## r_Site_ID[SENBARUOP,TyphoonPost] 0.67 -0.48
## r_Site_ID[SUEYOSHIFR,TyphoonPost] 0.64 -1.14
## r_Site_ID[TAKEYANBARUF,TyphoonPost] 1.12 -1.89
## r_Site_ID[TAMAGUSUKUFR,TyphoonPost] 0.66 -1.51
## r_Site_ID[TOUNANOP,TyphoonPost] 0.64 -1.42
## r_Site_ID[UEHARAFR,TyphoonPost] 0.72 -0.25
## r_Site_ID[YACHOOP,TyphoonPost] 0.75 -0.02
## r_Site_ID[YONAFR,TyphoonPost] 1.21 -3.00
## r_Site_ID[CHATANOP,LanduseDeveloped:TyphoonPost] 2.04 -4.98
## r_Site_ID[GENKAOP,LanduseDeveloped:TyphoonPost] 1.59 -1.90
## r_Site_ID[GESASHIOP,LanduseDeveloped:TyphoonPost] 3.19 -4.74
## r_Site_ID[HEIWAOP,LanduseDeveloped:TyphoonPost] 2.37 -5.84
## r_Site_ID[HENTONAOP,LanduseDeveloped:TyphoonPost] 1.36 -3.99
## r_Site_ID[KATSUUDAKEFR,LanduseDeveloped:TyphoonPost] 1.25 -3.34
## r_Site_ID[KEMINFR,LanduseDeveloped:TyphoonPost] 1.50 -4.85
## r_Site_ID[KURASHIKIOP,LanduseDeveloped:TyphoonPost] 1.91 -3.66
## r_Site_ID[MANABIFR,LanduseDeveloped:TyphoonPost] 2.56 -0.49
## r_Site_ID[NAGOFR,LanduseDeveloped:TyphoonPost] 2.06 -4.27
## r_Site_ID[NAKAGUSUKUFR,LanduseDeveloped:TyphoonPost] 1.90 -3.73
## r_Site_ID[OISTFR,LanduseDeveloped:TyphoonPost] 1.27 -3.45
## r_Site_ID[OISTOP,LanduseDeveloped:TyphoonPost] 1.24 -2.93
## r_Site_ID[OKUFR,LanduseDeveloped:TyphoonPost] 1.24 -3.15
## r_Site_ID[OYAMAOP,LanduseDeveloped:TyphoonPost] 1.90 -4.18
## r_Site_ID[SEFAFR,LanduseDeveloped:TyphoonPost] 2.17 -3.69
## r_Site_ID[SENBARUOP,LanduseDeveloped:TyphoonPost] 1.93 -4.56
## r_Site_ID[SUEYOSHIFR,LanduseDeveloped:TyphoonPost] 1.87 -4.08
## r_Site_ID[TAKEYANBARUF,LanduseDeveloped:TyphoonPost] 1.27 -2.36
## r_Site_ID[TAMAGUSUKUFR,LanduseDeveloped:TyphoonPost] 1.87 -3.79
## r_Site_ID[TOUNANOP,LanduseDeveloped:TyphoonPost] 1.84 -3.96
## r_Site_ID[UEHARAFR,LanduseDeveloped:TyphoonPost] 2.08 -5.10
## r_Site_ID[YACHOOP,LanduseDeveloped:TyphoonPost] 2.12 -5.19
## r_Site_ID[YONAFR,LanduseDeveloped:TyphoonPost] 1.44 -4.01
## lprior 1.12 -21.26
## lp__ 15.30 -106.78
## 25% 50%
## b_Intercept 0.30 0.48
## b_LanduseDeveloped -1.44 -0.98
## b_TyphoonPost -1.01 -0.72
## b_LanduseDeveloped:TyphoonPost -0.27 0.34
## sd_Site_ID__Intercept 0.66 0.81
## sd_Site_ID__LanduseDeveloped 1.34 1.81
## sd_Site_ID__TyphoonPost 1.12 1.38
## sd_Site_ID__LanduseDeveloped:TyphoonPost 1.10 1.82
## cor_Site_ID__Intercept__LanduseDeveloped -0.42 -0.11
## cor_Site_ID__Intercept__TyphoonPost 0.33 0.55
## cor_Site_ID__LanduseDeveloped__TyphoonPost -0.55 -0.27
## cor_Site_ID__Intercept__LanduseDeveloped:TyphoonPost -0.47 -0.16
## cor_Site_ID__LanduseDeveloped__LanduseDeveloped:TyphoonPost -0.58 -0.33
## cor_Site_ID__TyphoonPost__LanduseDeveloped:TyphoonPost -0.38 -0.06
## phi 12.07 17.50
## r_Site_ID[CHATANOP,Intercept] 0.34 0.62
## r_Site_ID[GENKAOP,Intercept] 0.01 0.58
## r_Site_ID[GESASHIOP,Intercept] -1.78 -1.41
## r_Site_ID[HEIWAOP,Intercept] 0.84 1.19
## r_Site_ID[HENTONAOP,Intercept] -0.58 -0.16
## r_Site_ID[KATSUUDAKEFR,Intercept] -0.45 -0.05
## r_Site_ID[KEMINFR,Intercept] -0.58 -0.13
## r_Site_ID[KURASHIKIOP,Intercept] -0.75 -0.47
## r_Site_ID[MANABIFR,Intercept] -1.28 -0.26
## r_Site_ID[NAGOFR,Intercept] -0.89 -0.58
## r_Site_ID[NAKAGUSUKUFR,Intercept] -0.71 -0.42
## r_Site_ID[OISTFR,Intercept] -0.44 -0.05
## r_Site_ID[OISTOP,Intercept] -0.21 0.20
## r_Site_ID[OKUFR,Intercept] -0.43 -0.04
## r_Site_ID[OYAMAOP,Intercept] 0.09 0.36
## r_Site_ID[SEFAFR,Intercept] -1.04 -0.74
## r_Site_ID[SENBARUOP,Intercept] 0.00 0.27
## r_Site_ID[SUEYOSHIFR,Intercept] -0.11 0.14
## r_Site_ID[TAKEYANBARUF,Intercept] -0.14 0.30
## r_Site_ID[TAMAGUSUKUFR,Intercept] -0.54 -0.27
## r_Site_ID[TOUNANOP,Intercept] -0.10 0.15
## r_Site_ID[UEHARAFR,Intercept] 0.41 0.70
## r_Site_ID[YACHOOP,Intercept] 0.14 0.42
## r_Site_ID[YONAFR,Intercept] -0.77 -0.30
## r_Site_ID[CHATANOP,LanduseDeveloped] -1.38 -0.20
## r_Site_ID[GENKAOP,LanduseDeveloped] 0.52 1.27
## r_Site_ID[GESASHIOP,LanduseDeveloped] -0.62 0.92
## r_Site_ID[HEIWAOP,LanduseDeveloped] -1.72 -0.26
## r_Site_ID[HENTONAOP,LanduseDeveloped] -0.52 0.08
## r_Site_ID[KATSUUDAKEFR,LanduseDeveloped] -0.36 0.19
## r_Site_ID[KEMINFR,LanduseDeveloped] -0.03 0.59
## r_Site_ID[KURASHIKIOP,LanduseDeveloped] -0.85 0.07
## r_Site_ID[MANABIFR,LanduseDeveloped] -5.18 -3.96
## r_Site_ID[NAGOFR,LanduseDeveloped] -1.05 -0.04
## r_Site_ID[NAKAGUSUKUFR,LanduseDeveloped] -0.86 0.05
## r_Site_ID[OISTFR,LanduseDeveloped] -0.35 0.21
## r_Site_ID[OISTOP,LanduseDeveloped] 0.23 0.84
## r_Site_ID[OKUFR,LanduseDeveloped] -0.45 0.11
## r_Site_ID[OYAMAOP,LanduseDeveloped] -0.97 0.02
## r_Site_ID[SEFAFR,LanduseDeveloped] -0.70 0.31
## r_Site_ID[SENBARUOP,LanduseDeveloped] -1.25 -0.18
## r_Site_ID[SUEYOSHIFR,LanduseDeveloped] -0.99 -0.02
## r_Site_ID[TAKEYANBARUF,LanduseDeveloped] 0.21 0.84
## r_Site_ID[TAMAGUSUKUFR,LanduseDeveloped] -0.88 0.03
## r_Site_ID[TOUNANOP,LanduseDeveloped] -0.90 0.03
## r_Site_ID[UEHARAFR,LanduseDeveloped] -1.48 -0.25
## r_Site_ID[YACHOOP,LanduseDeveloped] -1.55 -0.35
## r_Site_ID[YONAFR,LanduseDeveloped] -1.22 -0.52
## r_Site_ID[CHATANOP,TyphoonPost] 0.45 0.88
## r_Site_ID[GENKAOP,TyphoonPost] 0.07 0.95
## r_Site_ID[GESASHIOP,TyphoonPost] -3.95 -3.25
## r_Site_ID[HEIWAOP,TyphoonPost] 0.71 1.21
## r_Site_ID[HENTONAOP,TyphoonPost] -1.25 -0.53
## r_Site_ID[KATSUUDAKEFR,TyphoonPost] -0.94 -0.29
## r_Site_ID[KEMINFR,TyphoonPost] -1.59 -0.75
## r_Site_ID[KURASHIKIOP,TyphoonPost] -0.83 -0.38
## r_Site_ID[MANABIFR,TyphoonPost] -0.21 1.38
## r_Site_ID[NAGOFR,TyphoonPost] -0.34 0.11
## r_Site_ID[NAKAGUSUKUFR,TyphoonPost] -0.73 -0.30
## r_Site_ID[OISTFR,TyphoonPost] -0.95 -0.28
## r_Site_ID[OISTOP,TyphoonPost] -0.59 0.06
## r_Site_ID[OKUFR,TyphoonPost] -0.85 -0.22
## r_Site_ID[OYAMAOP,TyphoonPost] -0.42 0.00
## r_Site_ID[SEFAFR,TyphoonPost] -1.82 -1.30
## r_Site_ID[SENBARUOP,TyphoonPost] 0.33 0.76
## r_Site_ID[SUEYOSHIFR,TyphoonPost] -0.27 0.13
## r_Site_ID[TAKEYANBARUF,TyphoonPost] -0.29 0.37
## r_Site_ID[TAMAGUSUKUFR,TyphoonPost] -0.59 -0.17
## r_Site_ID[TOUNANOP,TyphoonPost] -0.53 -0.11
## r_Site_ID[UEHARAFR,TyphoonPost] 0.60 1.05
## r_Site_ID[YACHOOP,TyphoonPost] 0.85 1.32
## r_Site_ID[YONAFR,TyphoonPost] -1.33 -0.58
## r_Site_ID[CHATANOP,LanduseDeveloped:TyphoonPost] -1.27 -0.10
## r_Site_ID[GENKAOP,LanduseDeveloped:TyphoonPost] -0.08 0.70
## r_Site_ID[GESASHIOP,LanduseDeveloped:TyphoonPost] -1.00 0.21
## r_Site_ID[HEIWAOP,LanduseDeveloped:TyphoonPost] -1.75 -0.23
## r_Site_ID[HENTONAOP,LanduseDeveloped:TyphoonPost] -1.61 -0.65
## r_Site_ID[KATSUUDAKEFR,LanduseDeveloped:TyphoonPost] -1.20 -0.36
## r_Site_ID[KEMINFR,LanduseDeveloped:TyphoonPost] -2.26 -1.15
## r_Site_ID[KURASHIKIOP,LanduseDeveloped:TyphoonPost] -0.73 0.08
## r_Site_ID[MANABIFR,LanduseDeveloped:TyphoonPost] 1.44 3.25
## r_Site_ID[NAGOFR,LanduseDeveloped:TyphoonPost] -0.80 0.09
## r_Site_ID[NAKAGUSUKUFR,LanduseDeveloped:TyphoonPost] -0.75 0.07
## r_Site_ID[OISTFR,LanduseDeveloped:TyphoonPost] -1.23 -0.37
## r_Site_ID[OISTOP,LanduseDeveloped:TyphoonPost] -0.91 -0.15
## r_Site_ID[OKUFR,LanduseDeveloped:TyphoonPost] -1.04 -0.25
## r_Site_ID[OYAMAOP,LanduseDeveloped:TyphoonPost] -1.04 -0.06
## r_Site_ID[SEFAFR,LanduseDeveloped:TyphoonPost] -0.77 0.11
## r_Site_ID[SENBARUOP,LanduseDeveloped:TyphoonPost] -1.05 -0.02
## r_Site_ID[SUEYOSHIFR,LanduseDeveloped:TyphoonPost] -0.93 -0.02
## r_Site_ID[TAKEYANBARUF,LanduseDeveloped:TyphoonPost] -0.48 0.13
## r_Site_ID[TAMAGUSUKUFR,LanduseDeveloped:TyphoonPost] -0.79 0.04
## r_Site_ID[TOUNANOP,LanduseDeveloped:TyphoonPost] -0.94 -0.03
## r_Site_ID[UEHARAFR,LanduseDeveloped:TyphoonPost] -1.33 -0.11
## r_Site_ID[YACHOOP,LanduseDeveloped:TyphoonPost] -1.21 -0.04
## r_Site_ID[YONAFR,LanduseDeveloped:TyphoonPost] -1.45 -0.49
## lprior -19.50 -18.73
## lp__ -87.99 -77.84
## 75% 97.5% n_eff
## b_Intercept 0.65 1.04 39969
## b_LanduseDeveloped -0.51 0.46 51773
## b_TyphoonPost -0.44 0.16 42360
## b_LanduseDeveloped:TyphoonPost 0.95 2.24 29687
## sd_Site_ID__Intercept 1.00 1.51 19674
## sd_Site_ID__LanduseDeveloped 2.29 3.41 28493
## sd_Site_ID__TyphoonPost 1.68 2.38 21191
## sd_Site_ID__LanduseDeveloped:TyphoonPost 2.56 4.16 10957
## cor_Site_ID__Intercept__LanduseDeveloped 0.23 0.76 28295
## cor_Site_ID__Intercept__TyphoonPost 0.72 0.92 37739
## cor_Site_ID__LanduseDeveloped__TyphoonPost 0.07 0.70 30385
## cor_Site_ID__Intercept__LanduseDeveloped:TyphoonPost 0.20 0.76 38114
## cor_Site_ID__LanduseDeveloped__LanduseDeveloped:TyphoonPost -0.04 0.54 58399
## cor_Site_ID__TyphoonPost__LanduseDeveloped:TyphoonPost 0.28 0.78 24337
## phi 25.82 59.94 3222
## r_Site_ID[CHATANOP,Intercept] 0.92 1.60 51778
## r_Site_ID[GENKAOP,Intercept] 1.16 2.45 35699
## r_Site_ID[GESASHIOP,Intercept] -1.06 -0.36 48564
## r_Site_ID[HEIWAOP,Intercept] 1.55 2.35 23438
## r_Site_ID[HENTONAOP,Intercept] 0.26 1.28 46109
## r_Site_ID[KATSUUDAKEFR,Intercept] 0.34 1.31 48092
## r_Site_ID[KEMINFR,Intercept] 0.34 1.43 40070
## r_Site_ID[KURASHIKIOP,Intercept] -0.20 0.33 56401
## r_Site_ID[MANABIFR,Intercept] 0.63 2.26 24156
## r_Site_ID[NAGOFR,Intercept] -0.28 0.25 41123
## r_Site_ID[NAKAGUSUKUFR,Intercept] -0.15 0.36 45180
## r_Site_ID[OISTFR,Intercept] 0.35 1.38 19957
## r_Site_ID[OISTOP,Intercept] 0.64 1.70 43908
## r_Site_ID[OKUFR,Intercept] 0.34 1.31 45855
## r_Site_ID[OYAMAOP,Intercept] 0.65 1.31 24399
## r_Site_ID[SEFAFR,Intercept] -0.46 0.07 41610
## r_Site_ID[SENBARUOP,Intercept] 0.54 1.15 57493
## r_Site_ID[SUEYOSHIFR,Intercept] 0.41 0.99 62701
## r_Site_ID[TAKEYANBARUF,Intercept] 0.76 1.84 44572
## r_Site_ID[TAMAGUSUKUFR,Intercept] -0.01 0.53 63751
## r_Site_ID[TOUNANOP,Intercept] 0.42 1.01 54322
## r_Site_ID[UEHARAFR,Intercept] 1.00 1.69 46173
## r_Site_ID[YACHOOP,Intercept] 0.71 1.39 59294
## r_Site_ID[YONAFR,Intercept] 0.14 1.23 21310
## r_Site_ID[CHATANOP,LanduseDeveloped] 0.74 3.23 46492
## r_Site_ID[GENKAOP,LanduseDeveloped] 2.11 3.95 43483
## r_Site_ID[GESASHIOP,LanduseDeveloped] 2.85 6.98 35187
## r_Site_ID[HEIWAOP,LanduseDeveloped] 0.84 3.50 42484
## r_Site_ID[HENTONAOP,LanduseDeveloped] 0.70 2.11 52443
## r_Site_ID[KATSUUDAKEFR,LanduseDeveloped] 0.81 2.23 56787
## r_Site_ID[KEMINFR,LanduseDeveloped] 1.28 2.79 46625
## r_Site_ID[KURASHIKIOP,LanduseDeveloped] 1.14 3.91 58859
## r_Site_ID[MANABIFR,LanduseDeveloped] -2.76 -0.28 21164
## r_Site_ID[NAGOFR,LanduseDeveloped] 1.05 4.06 47576
## r_Site_ID[NAKAGUSUKUFR,LanduseDeveloped] 1.12 3.89 30861
## r_Site_ID[OISTFR,LanduseDeveloped] 0.83 2.25 29763
## r_Site_ID[OISTOP,LanduseDeveloped] 1.52 3.08 49699
## r_Site_ID[OKUFR,LanduseDeveloped] 0.72 2.10 49880
## r_Site_ID[OYAMAOP,LanduseDeveloped] 0.96 3.45 61956
## r_Site_ID[SEFAFR,LanduseDeveloped] 1.60 4.66 46953
## r_Site_ID[SENBARUOP,LanduseDeveloped] 0.73 3.24 56995
## r_Site_ID[SUEYOSHIFR,LanduseDeveloped] 0.88 3.37 60983
## r_Site_ID[TAKEYANBARUF,LanduseDeveloped] 1.54 3.11 53004
## r_Site_ID[TAMAGUSUKUFR,LanduseDeveloped] 1.03 3.67 60931
## r_Site_ID[TOUNANOP,LanduseDeveloped] 0.97 3.45 61492
## r_Site_ID[UEHARAFR,LanduseDeveloped] 0.72 3.20 45642
## r_Site_ID[YACHOOP,LanduseDeveloped] 0.64 3.22 49374
## r_Site_ID[YONAFR,LanduseDeveloped] 0.09 1.43 31182
## r_Site_ID[CHATANOP,TyphoonPost] 1.35 2.36 55143
## r_Site_ID[GENKAOP,TyphoonPost] 1.82 3.68 23882
## r_Site_ID[GESASHIOP,TyphoonPost] -2.58 -1.19 24311
## r_Site_ID[HEIWAOP,TyphoonPost] 1.75 2.96 60950
## r_Site_ID[HENTONAOP,TyphoonPost] 0.16 1.74 41066
## r_Site_ID[KATSUUDAKEFR,TyphoonPost] 0.34 1.84 40582
## r_Site_ID[KEMINFR,TyphoonPost] 0.04 1.72 29532
## r_Site_ID[KURASHIKIOP,TyphoonPost] 0.04 0.89 56582
## r_Site_ID[MANABIFR,TyphoonPost] 2.99 5.76 28078
## r_Site_ID[NAGOFR,TyphoonPost] 0.57 1.50 29758
## r_Site_ID[NAKAGUSUKUFR,TyphoonPost] 0.12 0.98 50204
## r_Site_ID[OISTFR,TyphoonPost] 0.35 1.90 20028
## r_Site_ID[OISTOP,TyphoonPost] 0.71 2.22 49908
## r_Site_ID[OKUFR,TyphoonPost] 0.40 1.88 34730
## r_Site_ID[OYAMAOP,TyphoonPost] 0.41 1.29 66031
## r_Site_ID[SEFAFR,TyphoonPost] -0.82 0.07 58309
## r_Site_ID[SENBARUOP,TyphoonPost] 1.21 2.17 55310
## r_Site_ID[SUEYOSHIFR,TyphoonPost] 0.54 1.40 47840
## r_Site_ID[TAKEYANBARUF,TyphoonPost] 1.05 2.60 31191
## r_Site_ID[TAMAGUSUKUFR,TyphoonPost] 0.24 1.11 39410
## r_Site_ID[TOUNANOP,TyphoonPost] 0.29 1.13 64400
## r_Site_ID[UEHARAFR,TyphoonPost] 1.53 2.60 53503
## r_Site_ID[YACHOOP,TyphoonPost] 1.83 2.92 34917
## r_Site_ID[YONAFR,TyphoonPost] 0.14 1.89 12832
## r_Site_ID[CHATANOP,LanduseDeveloped:TyphoonPost] 0.74 3.62 13234
## r_Site_ID[GENKAOP,LanduseDeveloped:TyphoonPost] 1.82 4.54 32021
## r_Site_ID[GESASHIOP,LanduseDeveloped:TyphoonPost] 2.30 8.37 8359
## r_Site_ID[HEIWAOP,LanduseDeveloped:TyphoonPost] 0.74 4.00 43929
## r_Site_ID[HENTONAOP,LanduseDeveloped:TyphoonPost] 0.00 1.50 22671
## r_Site_ID[KATSUUDAKEFR,LanduseDeveloped:TyphoonPost] 0.18 1.84 24925
## r_Site_ID[KEMINFR,LanduseDeveloped:TyphoonPost] -0.28 0.94 22500
## r_Site_ID[KURASHIKIOP,LanduseDeveloped:TyphoonPost] 1.13 4.37 59068
## r_Site_ID[MANABIFR,LanduseDeveloped:TyphoonPost] 5.16 8.88 24223
## r_Site_ID[NAGOFR,LanduseDeveloped:TyphoonPost] 1.20 4.49 15106
## r_Site_ID[NAKAGUSUKUFR,LanduseDeveloped:TyphoonPost] 1.09 4.28 58618
## r_Site_ID[OISTFR,LanduseDeveloped:TyphoonPost] 0.17 1.81 17676
## r_Site_ID[OISTOP,LanduseDeveloped:TyphoonPost] 0.43 2.25 32230
## r_Site_ID[OKUFR,LanduseDeveloped:TyphoonPost] 0.29 2.02 18362
## r_Site_ID[OYAMAOP,LanduseDeveloped:TyphoonPost] 0.79 3.87 54635
## r_Site_ID[SEFAFR,LanduseDeveloped:TyphoonPost] 1.40 5.39 25446
## r_Site_ID[SENBARUOP,LanduseDeveloped:TyphoonPost] 0.81 3.67 19804
## r_Site_ID[SUEYOSHIFR,LanduseDeveloped:TyphoonPost] 0.80 3.77 16956
## r_Site_ID[TAKEYANBARUF,LanduseDeveloped:TyphoonPost] 0.88 2.93 46139
## r_Site_ID[TAMAGUSUKUFR,LanduseDeveloped:TyphoonPost] 1.00 4.12 31516
## r_Site_ID[TOUNANOP,LanduseDeveloped:TyphoonPost] 0.82 3.84 49941
## r_Site_ID[UEHARAFR,LanduseDeveloped:TyphoonPost] 0.74 3.66 22931
## r_Site_ID[YACHOOP,LanduseDeveloped:TyphoonPost] 0.84 3.75 11081
## r_Site_ID[YONAFR,LanduseDeveloped:TyphoonPost] 0.14 1.86 6708
## lprior -18.03 -16.82 8766
## lp__ -67.62 -46.57 7008
## Rhat
## b_Intercept 1
## b_LanduseDeveloped 1
## b_TyphoonPost 1
## b_LanduseDeveloped:TyphoonPost 1
## sd_Site_ID__Intercept 1
## sd_Site_ID__LanduseDeveloped 1
## sd_Site_ID__TyphoonPost 1
## sd_Site_ID__LanduseDeveloped:TyphoonPost 1
## cor_Site_ID__Intercept__LanduseDeveloped 1
## cor_Site_ID__Intercept__TyphoonPost 1
## cor_Site_ID__LanduseDeveloped__TyphoonPost 1
## cor_Site_ID__Intercept__LanduseDeveloped:TyphoonPost 1
## cor_Site_ID__LanduseDeveloped__LanduseDeveloped:TyphoonPost 1
## cor_Site_ID__TyphoonPost__LanduseDeveloped:TyphoonPost 1
## phi 1
## r_Site_ID[CHATANOP,Intercept] 1
## r_Site_ID[GENKAOP,Intercept] 1
## r_Site_ID[GESASHIOP,Intercept] 1
## r_Site_ID[HEIWAOP,Intercept] 1
## r_Site_ID[HENTONAOP,Intercept] 1
## r_Site_ID[KATSUUDAKEFR,Intercept] 1
## r_Site_ID[KEMINFR,Intercept] 1
## r_Site_ID[KURASHIKIOP,Intercept] 1
## r_Site_ID[MANABIFR,Intercept] 1
## r_Site_ID[NAGOFR,Intercept] 1
## r_Site_ID[NAKAGUSUKUFR,Intercept] 1
## r_Site_ID[OISTFR,Intercept] 1
## r_Site_ID[OISTOP,Intercept] 1
## r_Site_ID[OKUFR,Intercept] 1
## r_Site_ID[OYAMAOP,Intercept] 1
## r_Site_ID[SEFAFR,Intercept] 1
## r_Site_ID[SENBARUOP,Intercept] 1
## r_Site_ID[SUEYOSHIFR,Intercept] 1
## r_Site_ID[TAKEYANBARUF,Intercept] 1
## r_Site_ID[TAMAGUSUKUFR,Intercept] 1
## r_Site_ID[TOUNANOP,Intercept] 1
## r_Site_ID[UEHARAFR,Intercept] 1
## r_Site_ID[YACHOOP,Intercept] 1
## r_Site_ID[YONAFR,Intercept] 1
## r_Site_ID[CHATANOP,LanduseDeveloped] 1
## r_Site_ID[GENKAOP,LanduseDeveloped] 1
## r_Site_ID[GESASHIOP,LanduseDeveloped] 1
## r_Site_ID[HEIWAOP,LanduseDeveloped] 1
## r_Site_ID[HENTONAOP,LanduseDeveloped] 1
## r_Site_ID[KATSUUDAKEFR,LanduseDeveloped] 1
## r_Site_ID[KEMINFR,LanduseDeveloped] 1
## r_Site_ID[KURASHIKIOP,LanduseDeveloped] 1
## r_Site_ID[MANABIFR,LanduseDeveloped] 1
## r_Site_ID[NAGOFR,LanduseDeveloped] 1
## r_Site_ID[NAKAGUSUKUFR,LanduseDeveloped] 1
## r_Site_ID[OISTFR,LanduseDeveloped] 1
## r_Site_ID[OISTOP,LanduseDeveloped] 1
## r_Site_ID[OKUFR,LanduseDeveloped] 1
## r_Site_ID[OYAMAOP,LanduseDeveloped] 1
## r_Site_ID[SEFAFR,LanduseDeveloped] 1
## r_Site_ID[SENBARUOP,LanduseDeveloped] 1
## r_Site_ID[SUEYOSHIFR,LanduseDeveloped] 1
## r_Site_ID[TAKEYANBARUF,LanduseDeveloped] 1
## r_Site_ID[TAMAGUSUKUFR,LanduseDeveloped] 1
## r_Site_ID[TOUNANOP,LanduseDeveloped] 1
## r_Site_ID[UEHARAFR,LanduseDeveloped] 1
## r_Site_ID[YACHOOP,LanduseDeveloped] 1
## r_Site_ID[YONAFR,LanduseDeveloped] 1
## r_Site_ID[CHATANOP,TyphoonPost] 1
## r_Site_ID[GENKAOP,TyphoonPost] 1
## r_Site_ID[GESASHIOP,TyphoonPost] 1
## r_Site_ID[HEIWAOP,TyphoonPost] 1
## r_Site_ID[HENTONAOP,TyphoonPost] 1
## r_Site_ID[KATSUUDAKEFR,TyphoonPost] 1
## r_Site_ID[KEMINFR,TyphoonPost] 1
## r_Site_ID[KURASHIKIOP,TyphoonPost] 1
## r_Site_ID[MANABIFR,TyphoonPost] 1
## r_Site_ID[NAGOFR,TyphoonPost] 1
## r_Site_ID[NAKAGUSUKUFR,TyphoonPost] 1
## r_Site_ID[OISTFR,TyphoonPost] 1
## r_Site_ID[OISTOP,TyphoonPost] 1
## r_Site_ID[OKUFR,TyphoonPost] 1
## r_Site_ID[OYAMAOP,TyphoonPost] 1
## r_Site_ID[SEFAFR,TyphoonPost] 1
## r_Site_ID[SENBARUOP,TyphoonPost] 1
## r_Site_ID[SUEYOSHIFR,TyphoonPost] 1
## r_Site_ID[TAKEYANBARUF,TyphoonPost] 1
## r_Site_ID[TAMAGUSUKUFR,TyphoonPost] 1
## r_Site_ID[TOUNANOP,TyphoonPost] 1
## r_Site_ID[UEHARAFR,TyphoonPost] 1
## r_Site_ID[YACHOOP,TyphoonPost] 1
## r_Site_ID[YONAFR,TyphoonPost] 1
## r_Site_ID[CHATANOP,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[GENKAOP,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[GESASHIOP,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[HEIWAOP,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[HENTONAOP,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[KATSUUDAKEFR,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[KEMINFR,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[KURASHIKIOP,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[MANABIFR,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[NAGOFR,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[NAKAGUSUKUFR,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[OISTFR,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[OISTOP,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[OKUFR,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[OYAMAOP,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[SEFAFR,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[SENBARUOP,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[SUEYOSHIFR,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[TAKEYANBARUF,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[TAMAGUSUKUFR,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[TOUNANOP,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[UEHARAFR,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[YACHOOP,LanduseDeveloped:TyphoonPost] 1
## r_Site_ID[YONAFR,LanduseDeveloped:TyphoonPost] 1
## lprior 1
## lp__ 1
##
## Samples were drawn using NUTS(diag_e) at Mon Nov 14 13:40:56 2022.
## For each parameter, n_eff is a crude measure of effective sample size,
## and Rhat is the potential scale reduction factor on split chains (at
## convergence, Rhat=1).bayes_R2(mod_nonspatial_beta) # pseudo r-squared of model## Estimate Est.Error Q2.5 Q97.5
## R2 0.8772635 0.06431607 0.7162266 0.9653288# simulate data from 500 random draws of posterior and compare it to observed data
# the black line should run through the center of the blue lines
pp_check(mod_nonspatial_beta, ndraws = 500) + theme_cowplot()
## Spatial Autocorrelation test
bres <- residuals(mod_nonspatial_beta)[,"Estimate"]
d_mat = as.matrix(dist(df[,c("Lat", "Long")], diag=T, upper=T)) # make distance matrix
d_mat_inv <- 1/d_mat
d_mat_inv[which(d_mat_inv == Inf)] <- 0
Moran.I(bres, d_mat_inv) # non-significant = no spatial autocorrelation## $observed
## [1] -0.03423118
##
## $expected
## [1] -0.0212766
##
## $sd
## [1] 0.0404324
##
## $p.value
## [1] 0.7486642acoustic index resistance
NDSI - no effect of land use
# get pre vs post data for comparison
df<-tidy.stability_AI %>%
filter(Index %in% "NDSI" & response_variable %in% 'Resist')
df<-df[complete.cases(df),]
# change levels for model
df$Landuse<-df$Landuse %>% parse_character() %>% parse_factor(levels = c('Forest','Developed'))
#df$site_id<-df$site_id %>% parse_character() %>% parse_factor(levels = c(df$site_id[order(df$PC1)]))
mod_nonspatial_beta <-
brm(data = df, family = Beta(),
Stability ~ 1 + Landuse + (1|Site_ID),
iter = 5e4, warmup = 5000, chains = 4, cores = 4, thin = 2,
seed = 666)## Compiling Stan program...## Start sampling## Warning: There were 57 divergent transitions after warmup. See
## https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## to find out why this is a problem and how to eliminate them.## Warning: Examine the pairs() plot to diagnose sampling problemsmod_nonspatial_beta <- add_criterion(mod_nonspatial_beta, "loo")## Warning: Found 23 observations with a pareto_k > 0.7 in model
## 'mod_nonspatial_beta'. It is recommended to set 'moment_match = TRUE' in order
## to perform moment matching for problematic observations.#check MCMC traces
mcmc_plot(mod_nonspatial_beta, type = "trace") + theme_cowplot()
#check for agreement of chains
mcmc_plot(mod_nonspatial_beta, type = "dens_overlay") + theme_cowplot()
# plot posterior estimates of fixed effects
mcmc_plot(mod_nonspatial_beta, type = "intervals",prob = 0.68, prob_outer = 0.95, variable = "^b_", regex = TRUE) + theme_cowplot()
## no effects ##
## Parameter fits and stats
mod_nonspatial_beta$fit # Rhat values closer to 1 and n_eff values > 1000 are ideal.## Inference for Stan model: 516dc2723972b0b4959c45f1ed5792b7.
## 4 chains, each with iter=50000; warmup=5000; thin=2;
## post-warmup draws per chain=22500, total post-warmup draws=90000.
##
## mean se_mean sd 2.5% 25% 50%
## b_Intercept 0.66 0.00 0.48 -0.29 0.34 0.65
## b_LanduseDeveloped 0.04 0.00 0.77 -1.51 -0.46 0.04
## sd_Site_ID__Intercept 1.60 0.00 0.37 0.96 1.34 1.57
## phi 16.13 0.32 17.38 2.81 6.58 10.87
## r_Site_ID[CHATANOP,Intercept] 0.63 0.00 0.78 -0.85 0.12 0.61
## r_Site_ID[GENKAOP,Intercept] -0.44 0.00 0.81 -1.99 -0.97 -0.45
## r_Site_ID[GESASHIOP,Intercept] 1.30 0.00 0.85 -0.30 0.73 1.28
## r_Site_ID[HEIWAOP,Intercept] 0.84 0.00 0.80 -0.68 0.31 0.82
## r_Site_ID[HENTONAOP,Intercept] 1.13 0.00 0.89 -0.55 0.53 1.10
## r_Site_ID[KATSUUDAKEFR,Intercept] -0.01 0.00 0.81 -1.58 -0.54 -0.02
## r_Site_ID[KEMINFR,Intercept] -0.96 0.00 0.83 -2.57 -1.51 -0.97
## r_Site_ID[KURASHIKIOP,Intercept] 0.25 0.00 0.76 -1.20 -0.24 0.23
## r_Site_ID[MANABIFR,Intercept] 0.07 0.00 0.81 -1.50 -0.47 0.05
## r_Site_ID[NAGOFR,Intercept] 1.22 0.00 0.84 -0.35 0.65 1.19
## r_Site_ID[NAKAGUSUKUFR,Intercept] -0.46 0.00 0.75 -1.89 -0.95 -0.48
## r_Site_ID[OISTFR,Intercept] 0.44 0.00 0.83 -1.14 -0.11 0.42
## r_Site_ID[OISTOP,Intercept] -0.84 0.00 0.82 -2.43 -1.37 -0.84
## r_Site_ID[OKUFR,Intercept] -0.18 0.00 0.81 -1.74 -0.71 -0.19
## r_Site_ID[OYAMAOP,Intercept] 0.43 0.00 0.76 -1.03 -0.07 0.41
## r_Site_ID[SEFAFR,Intercept] -0.48 0.00 0.75 -1.92 -0.97 -0.49
## r_Site_ID[SENBARUOP,Intercept] 0.27 0.00 0.76 -1.18 -0.23 0.25
## r_Site_ID[SUEYOSHIFR,Intercept] 2.12 0.01 0.96 0.35 1.46 2.07
## r_Site_ID[TAMAGUSUKUFR,Intercept] -0.95 0.00 0.77 -2.43 -1.45 -0.96
## r_Site_ID[TOUNANOP,Intercept] 0.38 0.00 0.76 -1.07 -0.12 0.36
## r_Site_ID[UEHARAFR,Intercept] -0.43 0.00 0.75 -1.88 -0.91 -0.44
## r_Site_ID[YACHOOP,Intercept] -4.99 0.01 1.19 -7.43 -5.76 -4.96
## r_Site_ID[YONAFR,Intercept] 0.91 0.00 0.86 -0.72 0.33 0.89
## lprior -10.69 0.01 1.01 -12.97 -11.27 -10.59
## lp__ -7.65 0.10 9.44 -24.95 -14.22 -8.12
## 75% 97.5% n_eff Rhat
## b_Intercept 0.97 1.63 39091 1
## b_LanduseDeveloped 0.55 1.55 37326 1
## sd_Site_ID__Intercept 1.83 2.42 19000 1
## phi 18.97 62.28 3041 1
## r_Site_ID[CHATANOP,Intercept] 1.12 2.24 54239 1
## r_Site_ID[GENKAOP,Intercept] 0.08 1.19 47576 1
## r_Site_ID[GESASHIOP,Intercept] 1.85 3.06 44418 1
## r_Site_ID[HEIWAOP,Intercept] 1.34 2.48 52281 1
## r_Site_ID[HENTONAOP,Intercept] 1.69 2.97 41639 1
## r_Site_ID[KATSUUDAKEFR,Intercept] 0.51 1.63 49696 1
## r_Site_ID[KEMINFR,Intercept] -0.43 0.70 42813 1
## r_Site_ID[KURASHIKIOP,Intercept] 0.73 1.84 58886 1
## r_Site_ID[MANABIFR,Intercept] 0.59 1.72 48155 1
## r_Site_ID[NAGOFR,Intercept] 1.75 2.94 43537 1
## r_Site_ID[NAKAGUSUKUFR,Intercept] 0.01 1.07 53911 1
## r_Site_ID[OISTFR,Intercept] 0.97 2.13 48141 1
## r_Site_ID[OISTOP,Intercept] -0.31 0.80 43900 1
## r_Site_ID[OKUFR,Intercept] 0.33 1.44 49247 1
## r_Site_ID[OYAMAOP,Intercept] 0.91 2.01 56413 1
## r_Site_ID[SEFAFR,Intercept] 0.00 1.06 54620 1
## r_Site_ID[SENBARUOP,Intercept] 0.74 1.83 55595 1
## r_Site_ID[SUEYOSHIFR,Intercept] 2.74 4.14 26531 1
## r_Site_ID[TAMAGUSUKUFR,Intercept] -0.45 0.63 48712 1
## r_Site_ID[TOUNANOP,Intercept] 0.86 1.95 56863 1
## r_Site_ID[UEHARAFR,Intercept] 0.04 1.10 52574 1
## r_Site_ID[YACHOOP,Intercept] -4.19 -2.74 15238 1
## r_Site_ID[YONAFR,Intercept] 1.46 2.69 45666 1
## lprior -9.99 -9.02 7599 1
## lp__ -1.46 12.01 9851 1
##
## Samples were drawn using NUTS(diag_e) at Mon Nov 14 13:43:20 2022.
## For each parameter, n_eff is a crude measure of effective sample size,
## and Rhat is the potential scale reduction factor on split chains (at
## convergence, Rhat=1).bayes_R2(mod_nonspatial_beta) # pseudo r-squared of model## Estimate Est.Error Q2.5 Q97.5
## R2 0.7788499 0.1228608 0.5023151 0.9590089# simulate data from 500 random draws of posterior and compare it to observed data
# the black line should run through the center of the blue lines
pp_check(mod_nonspatial_beta, ndraws = 500) + theme_cowplot()
## Spatial Autocorrelation test
bres <- residuals(mod_nonspatial_beta)[,"Estimate"]
d_mat = as.matrix(dist(df[,c("Lat", "Long")], diag=T, upper=T)) # make distance matrix
d_mat_inv <- 1/d_mat
d_mat_inv[which(d_mat_inv == Inf)] <- 0
Moran.I(bres, d_mat_inv) # non-significant = no spatial autocorrelation## $observed
## [1] -0.07751008
##
## $expected
## [1] -0.04545455
##
## $sd
## [1] 0.08880647
##
## $p.value
## [1] 0.7181298NDSI_Bio - no effect of land use
# get pre vs post data for comparison
df<-tidy.stability_AI %>%
filter(Index %in% "NDSI_Bio" & response_variable %in% 'Resist')
df<-df[complete.cases(df),]
# change levels for model
df$Landuse<-df$Landuse %>% parse_character() %>% parse_factor(levels = c('Forest','Developed'))
#df$site_id<-df$site_id %>% parse_character() %>% parse_factor(levels = c(df$site_id[order(df$PC1)]))
mod_nonspatial_beta <-
brm(data = df, family = Beta(),
Stability ~ 1 + Landuse + (1|Site_ID),
iter = 5e4, warmup = 5000, chains = 4, cores = 4, thin = 2,
seed = 666)## Compiling Stan program...## Start sampling## Warning: There were 102 divergent transitions after warmup. See
## https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## to find out why this is a problem and how to eliminate them.## Warning: There were 3 chains where the estimated Bayesian Fraction of Missing Information was low. See
## https://mc-stan.org/misc/warnings.html#bfmi-low## Warning: Examine the pairs() plot to diagnose sampling problemsmod_nonspatial_beta <- add_criterion(mod_nonspatial_beta, "loo")## Warning: Found 23 observations with a pareto_k > 0.7 in model
## 'mod_nonspatial_beta'. It is recommended to set 'moment_match = TRUE' in order
## to perform moment matching for problematic observations.#check MCMC traces
mcmc_plot(mod_nonspatial_beta, type = "trace") + theme_cowplot()
#check for agreement of chains
mcmc_plot(mod_nonspatial_beta, type = "dens_overlay") + theme_cowplot()
# plot posterior estimates of fixed effects
mcmc_plot(mod_nonspatial_beta, type = "intervals",prob = 0.68, prob_outer = 0.95, variable = "^b_", regex = TRUE) + theme_cowplot()
## no effects ##
## Parameter fits and stats
mod_nonspatial_beta$fit # Rhat values closer to 1 and n_eff values > 1000 are ideal.## Inference for Stan model: 516dc2723972b0b4959c45f1ed5792b7.
## 4 chains, each with iter=50000; warmup=5000; thin=2;
## post-warmup draws per chain=22500, total post-warmup draws=90000.
##
## mean se_mean sd 2.5% 25% 50%
## b_Intercept 0.10 0.00 0.55 -0.98 -0.26 0.09
## b_LanduseDeveloped 0.57 0.01 0.89 -1.17 -0.01 0.56
## sd_Site_ID__Intercept 1.85 0.01 0.49 0.95 1.53 1.83
## phi 16.86 0.44 20.44 1.92 5.25 9.93
## r_Site_ID[CHATANOP,Intercept] 1.87 0.01 1.01 -0.06 1.19 1.85
## r_Site_ID[GENKAOP,Intercept] -0.05 0.01 0.90 -1.81 -0.65 -0.07
## r_Site_ID[GESASHIOP,Intercept] 0.55 0.00 0.83 -1.07 0.01 0.54
## r_Site_ID[HEIWAOP,Intercept] 1.39 0.01 0.92 -0.40 0.78 1.38
## r_Site_ID[HENTONAOP,Intercept] -1.94 0.01 1.04 -3.97 -2.63 -1.95
## r_Site_ID[KATSUUDAKEFR,Intercept] 0.23 0.01 0.91 -1.54 -0.37 0.22
## r_Site_ID[KEMINFR,Intercept] -0.69 0.01 0.91 -2.47 -1.29 -0.69
## r_Site_ID[KURASHIKIOP,Intercept] 0.28 0.00 0.82 -1.33 -0.25 0.28
## r_Site_ID[MANABIFR,Intercept] 0.96 0.01 0.98 -0.89 0.31 0.93
## r_Site_ID[NAGOFR,Intercept] -0.54 0.00 0.83 -2.19 -1.07 -0.54
## r_Site_ID[NAKAGUSUKUFR,Intercept] 0.57 0.00 0.83 -1.06 0.03 0.57
## r_Site_ID[OISTFR,Intercept] -0.22 0.01 0.91 -1.98 -0.81 -0.23
## r_Site_ID[OISTOP,Intercept] 0.85 0.01 0.96 -0.97 0.20 0.82
## r_Site_ID[OKUFR,Intercept] 0.99 0.01 0.98 -0.87 0.33 0.96
## r_Site_ID[OYAMAOP,Intercept] 1.17 0.01 0.89 -0.55 0.58 1.17
## r_Site_ID[SEFAFR,Intercept] -4.60 0.01 1.35 -7.27 -5.50 -4.58
## r_Site_ID[SENBARUOP,Intercept] 2.98 0.01 1.21 0.72 2.15 2.93
## r_Site_ID[SUEYOSHIFR,Intercept] 0.79 0.00 0.85 -0.88 0.24 0.79
## r_Site_ID[TAMAGUSUKUFR,Intercept] -1.98 0.01 1.02 -3.98 -2.64 -1.99
## r_Site_ID[TOUNANOP,Intercept] 0.19 0.00 0.81 -1.41 -0.34 0.18
## r_Site_ID[UEHARAFR,Intercept] -2.41 0.01 1.10 -4.58 -3.14 -2.42
## r_Site_ID[YACHOOP,Intercept] -0.03 0.00 0.81 -1.64 -0.55 -0.03
## r_Site_ID[YONAFR,Intercept] 0.02 0.01 0.90 -1.73 -0.58 0.01
## lprior -10.66 0.02 1.23 -13.48 -11.40 -10.55
## lp__ -5.98 0.15 11.00 -26.09 -13.71 -6.45
## 75% 97.5% n_eff Rhat
## b_Intercept 0.45 1.20 24949 1
## b_LanduseDeveloped 1.14 2.34 22202 1
## sd_Site_ID__Intercept 2.15 2.89 7181 1
## phi 19.76 77.70 2185 1
## r_Site_ID[CHATANOP,Intercept] 2.53 3.89 18188 1
## r_Site_ID[GENKAOP,Intercept] 0.52 1.77 24213 1
## r_Site_ID[GESASHIOP,Intercept] 1.08 2.20 31930 1
## r_Site_ID[HEIWAOP,Intercept] 1.99 3.21 24679 1
## r_Site_ID[HENTONAOP,Intercept] -1.25 0.13 21629 1
## r_Site_ID[KATSUUDAKEFR,Intercept] 0.81 2.09 24358 1
## r_Site_ID[KEMINFR,Intercept] -0.10 1.14 25190 1
## r_Site_ID[KURASHIKIOP,Intercept] 0.81 1.92 42903 1
## r_Site_ID[MANABIFR,Intercept] 1.59 2.97 23867 1
## r_Site_ID[NAGOFR,Intercept] 0.00 1.10 41656 1
## r_Site_ID[NAKAGUSUKUFR,Intercept] 1.11 2.26 38836 1
## r_Site_ID[OISTFR,Intercept] 0.36 1.61 27779 1
## r_Site_ID[OISTOP,Intercept] 1.47 2.81 23772 1
## r_Site_ID[OKUFR,Intercept] 1.62 2.98 24971 1
## r_Site_ID[OYAMAOP,Intercept] 1.74 2.96 28678 1
## r_Site_ID[SEFAFR,Intercept] -3.68 -2.02 10581 1
## r_Site_ID[SENBARUOP,Intercept] 3.77 5.49 11749 1
## r_Site_ID[SUEYOSHIFR,Intercept] 1.34 2.49 36915 1
## r_Site_ID[TAMAGUSUKUFR,Intercept] -1.32 0.04 24999 1
## r_Site_ID[TOUNANOP,Intercept] 0.70 1.80 40779 1
## r_Site_ID[UEHARAFR,Intercept] -1.69 -0.25 18085 1
## r_Site_ID[YACHOOP,Intercept] 0.49 1.57 41825 1
## r_Site_ID[YONAFR,Intercept] 0.60 1.83 25486 1
## lprior -9.78 -8.60 4518 1
## lp__ 1.36 16.94 5310 1
##
## Samples were drawn using NUTS(diag_e) at Mon Nov 14 13:44:29 2022.
## For each parameter, n_eff is a crude measure of effective sample size,
## and Rhat is the potential scale reduction factor on split chains (at
## convergence, Rhat=1).bayes_R2(mod_nonspatial_beta) # pseudo r-squared of model## Estimate Est.Error Q2.5 Q97.5
## R2 0.8037031 0.1551114 0.4065787 0.9806057# simulate data from 500 random draws of posterior and compare it to observed data
# the black line should run through the center of the blue lines
pp_check(mod_nonspatial_beta, ndraws = 500) + theme_cowplot()
## Spatial Autocorrelation test
bres <- residuals(mod_nonspatial_beta)[,"Estimate"]
d_mat = as.matrix(dist(df[,c("Lat", "Long")], diag=T, upper=T)) # make distance matrix
d_mat_inv <- 1/d_mat
d_mat_inv[which(d_mat_inv == Inf)] <- 0
Moran.I(bres, d_mat_inv) # non-significant = no spatial autocorrelation## $observed
## [1] -0.1606967
##
## $expected
## [1] -0.04545455
##
## $sd
## [1] 0.08684368
##
## $p.value
## [1] 0.1845066NDSI_Anthro - no effect of land use
# get pre vs post data for comparison
df<-tidy.stability_AI %>%
filter(Index %in% "NDSI_Anth" & response_variable %in% 'Resist')
df<-df[complete.cases(df),]
# change levels for model
df$Landuse<-df$Landuse %>% parse_character() %>% parse_factor(levels = c('Forest','Developed'))
#df$site_id<-df$site_id %>% parse_character() %>% parse_factor(levels = c(df$site_id[order(df$PC1)]))
mod_nonspatial_beta <-
brm(data = df, family = Beta(),
Stability ~ 1 + Landuse + (1|Site_ID),
iter = 5e4, warmup = 5000, chains = 4, cores = 4, thin = 2,
seed = 666)## Compiling Stan program...## Start sampling## Warning: There were 90 divergent transitions after warmup. See
## https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## to find out why this is a problem and how to eliminate them.## Warning: Examine the pairs() plot to diagnose sampling problemsmod_nonspatial_beta <- add_criterion(mod_nonspatial_beta, "loo")## Warning: Found 23 observations with a pareto_k > 0.7 in model
## 'mod_nonspatial_beta'. It is recommended to set 'moment_match = TRUE' in order
## to perform moment matching for problematic observations.#check MCMC traces
mcmc_plot(mod_nonspatial_beta, type = "trace") + theme_cowplot()
#check for agreement of chains
mcmc_plot(mod_nonspatial_beta, type = "dens_overlay") + theme_cowplot()
# plot posterior estimates of fixed effects
mcmc_plot(mod_nonspatial_beta, type = "intervals",prob = 0.68, prob_outer = 0.95, variable = "^b_", regex = TRUE) + theme_cowplot()
## no effects ##
## Parameter fits and stats
mod_nonspatial_beta$fit # Rhat values closer to 1 and n_eff values > 1000 are ideal.## Inference for Stan model: 516dc2723972b0b4959c45f1ed5792b7.
## 4 chains, each with iter=50000; warmup=5000; thin=2;
## post-warmup draws per chain=22500, total post-warmup draws=90000.
##
## mean se_mean sd 2.5% 25% 50%
## b_Intercept 0.20 0.00 0.48 -0.73 -0.11 0.20
## b_LanduseDeveloped 0.30 0.00 0.77 -1.21 -0.20 0.30
## sd_Site_ID__Intercept 1.59 0.00 0.38 0.91 1.33 1.57
## phi 16.79 0.32 18.02 2.58 6.26 10.91
## r_Site_ID[CHATANOP,Intercept] -0.25 0.00 0.73 -1.70 -0.73 -0.26
## r_Site_ID[GENKAOP,Intercept] -0.44 0.00 0.80 -2.02 -0.96 -0.44
## r_Site_ID[GESASHIOP,Intercept] 0.25 0.00 0.74 -1.20 -0.23 0.24
## r_Site_ID[HEIWAOP,Intercept] 1.38 0.00 0.83 -0.22 0.83 1.37
## r_Site_ID[HENTONAOP,Intercept] 0.28 0.00 0.81 -1.29 -0.25 0.27
## r_Site_ID[KATSUUDAKEFR,Intercept] -1.16 0.00 0.84 -2.82 -1.71 -1.17
## r_Site_ID[KEMINFR,Intercept] 0.95 0.00 0.86 -0.68 0.38 0.94
## r_Site_ID[KURASHIKIOP,Intercept] -0.29 0.00 0.74 -1.74 -0.77 -0.30
## r_Site_ID[MANABIFR,Intercept] -1.14 0.01 0.84 -2.82 -1.69 -1.14
## r_Site_ID[NAGOFR,Intercept] -1.32 0.00 0.82 -2.93 -1.85 -1.33
## r_Site_ID[NAKAGUSUKUFR,Intercept] 1.65 0.00 0.88 -0.04 1.06 1.63
## r_Site_ID[OISTFR,Intercept] -0.15 0.00 0.80 -1.72 -0.67 -0.15
## r_Site_ID[OISTOP,Intercept] 1.31 0.00 0.90 -0.40 0.71 1.29
## r_Site_ID[OKUFR,Intercept] 0.05 0.00 0.81 -1.53 -0.48 0.04
## r_Site_ID[OYAMAOP,Intercept] 1.11 0.00 0.80 -0.44 0.59 1.10
## r_Site_ID[SEFAFR,Intercept] 0.10 0.00 0.73 -1.34 -0.37 0.09
## r_Site_ID[SENBARUOP,Intercept] -0.87 0.00 0.77 -2.37 -1.36 -0.87
## r_Site_ID[SUEYOSHIFR,Intercept] -0.31 0.00 0.74 -1.77 -0.79 -0.31
## r_Site_ID[TAMAGUSUKUFR,Intercept] 1.45 0.00 0.84 -0.18 0.89 1.44
## r_Site_ID[TOUNANOP,Intercept] 0.44 0.00 0.75 -1.02 -0.05 0.43
## r_Site_ID[UEHARAFR,Intercept] 1.36 0.00 0.83 -0.26 0.80 1.35
## r_Site_ID[YACHOOP,Intercept] -4.58 0.01 1.20 -6.99 -5.37 -4.56
## r_Site_ID[YONAFR,Intercept] 0.31 0.00 0.81 -1.26 -0.22 0.30
## lprior -10.66 0.01 1.06 -13.05 -11.30 -10.56
## lp__ -8.95 0.11 10.06 -27.44 -16.07 -9.39
## 75% 97.5% n_eff Rhat
## b_Intercept 0.51 1.15 37382 1
## b_LanduseDeveloped 0.80 1.84 27065 1
## sd_Site_ID__Intercept 1.83 2.42 15403 1
## phi 20.15 67.90 3248 1
## r_Site_ID[CHATANOP,Intercept] 0.22 1.21 54556 1
## r_Site_ID[GENKAOP,Intercept] 0.08 1.17 37123 1
## r_Site_ID[GESASHIOP,Intercept] 0.72 1.73 54220 1
## r_Site_ID[HEIWAOP,Intercept] 1.92 3.08 35682 1
## r_Site_ID[HENTONAOP,Intercept] 0.80 1.92 38900 1
## r_Site_ID[KATSUUDAKEFR,Intercept] -0.61 0.53 29879 1
## r_Site_ID[KEMINFR,Intercept] 1.50 2.70 40529 1
## r_Site_ID[KURASHIKIOP,Intercept] 0.18 1.18 52243 1
## r_Site_ID[MANABIFR,Intercept] -0.60 0.54 27794 1
## r_Site_ID[NAGOFR,Intercept] -0.80 0.32 40008 1
## r_Site_ID[NAKAGUSUKUFR,Intercept] 2.22 3.41 31968 1
## r_Site_ID[OISTFR,Intercept] 0.37 1.47 32720 1
## r_Site_ID[OISTOP,Intercept] 1.89 3.17 37156 1
## r_Site_ID[OKUFR,Intercept] 0.56 1.66 38228 1
## r_Site_ID[OYAMAOP,Intercept] 1.62 2.72 43373 1
## r_Site_ID[SEFAFR,Intercept] 0.57 1.58 52309 1
## r_Site_ID[SENBARUOP,Intercept] -0.37 0.66 46486 1
## r_Site_ID[SUEYOSHIFR,Intercept] 0.16 1.16 52892 1
## r_Site_ID[TAMAGUSUKUFR,Intercept] 1.99 3.15 35619 1
## r_Site_ID[TOUNANOP,Intercept] 0.91 1.94 50209 1
## r_Site_ID[UEHARAFR,Intercept] 1.89 3.04 35614 1
## r_Site_ID[YACHOOP,Intercept] -3.77 -2.29 13745 1
## r_Site_ID[YONAFR,Intercept] 0.83 1.94 38225 1
## lprior -9.91 -8.89 6793 1
## lp__ -2.19 11.60 8502 1
##
## Samples were drawn using NUTS(diag_e) at Mon Nov 14 13:45:32 2022.
## For each parameter, n_eff is a crude measure of effective sample size,
## and Rhat is the potential scale reduction factor on split chains (at
## convergence, Rhat=1).bayes_R2(mod_nonspatial_beta) # pseudo r-squared of model## Estimate Est.Error Q2.5 Q97.5
## R2 0.7788142 0.1466721 0.4306832 0.9682682# simulate data from 500 random draws of posterior and compare it to observed data
# the black line should run through the center of the blue lines
pp_check(mod_nonspatial_beta, ndraws = 500) + theme_cowplot()
## Spatial Autocorrelation test
bres <- residuals(mod_nonspatial_beta)[,"Estimate"]
d_mat = as.matrix(dist(df[,c("Lat", "Long")], diag=T, upper=T)) # make distance matrix
d_mat_inv <- 1/d_mat
d_mat_inv[which(d_mat_inv == Inf)] <- 0
Moran.I(bres, d_mat_inv) # non-significant = no spatial autocorrelation## $observed
## [1] -0.1208245
##
## $expected
## [1] -0.04545455
##
## $sd
## [1] 0.08851953
##
## $p.value
## [1] 0.3945194acoustic index recovery time
NDSI - no effect of land use
# get pre vs post data for comparison
df<-tidy.stability_AI %>%
filter(Index %in% "NDSI" & response_variable %in% 'Recov')
df<-df[complete.cases(df),]
# change levels for model
df$Landuse<-df$Landuse %>% parse_character() %>% parse_factor(levels = c('Forest','Developed'))
#df$site_id<-df$site_id %>% parse_character() %>% parse_factor(levels = c(df$site_id[order(df$PC1)]))
mod_nonspatial_beta <-
brm(data = df, family = Beta(),
Stability ~ 1 + Landuse + (1|Site_ID),
iter = 5e4, warmup = 5000, chains = 4, cores = 4, thin = 2,
seed = 666)## Compiling Stan program...## Start sampling## Warning: There were 31 divergent transitions after warmup. See
## https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## to find out why this is a problem and how to eliminate them.## Warning: Examine the pairs() plot to diagnose sampling problemsmod_nonspatial_beta <- add_criterion(mod_nonspatial_beta, "loo")## Warning: Found 20 observations with a pareto_k > 0.7 in model
## 'mod_nonspatial_beta'. It is recommended to set 'moment_match = TRUE' in order
## to perform moment matching for problematic observations.#check MCMC traces
mcmc_plot(mod_nonspatial_beta, type = "trace") + theme_cowplot()
#check for agreement of chains
mcmc_plot(mod_nonspatial_beta, type = "dens_overlay") + theme_cowplot()
# plot posterior estimates of fixed effects
mcmc_plot(mod_nonspatial_beta, type = "intervals",prob = 0.68, prob_outer = 0.95, variable = "^b_", regex = TRUE) + theme_cowplot()
## no effects ##
## Parameter fits and stats
mod_nonspatial_beta$fit # Rhat values closer to 1 and n_eff values > 1000 are ideal.## Inference for Stan model: 516dc2723972b0b4959c45f1ed5792b7.
## 4 chains, each with iter=50000; warmup=5000; thin=2;
## post-warmup draws per chain=22500, total post-warmup draws=90000.
##
## mean se_mean sd 2.5% 25% 50%
## b_Intercept 0.94 0.00 0.52 -0.08 0.60 0.93
## b_LanduseDeveloped 0.66 0.00 0.89 -1.10 0.08 0.66
## sd_Site_ID__Intercept 1.70 0.00 0.38 1.06 1.43 1.66
## phi 24.03 0.25 22.71 3.97 10.54 17.42
## r_Site_ID[CHATANOP,Intercept] 0.87 0.00 0.79 -0.62 0.34 0.85
## r_Site_ID[GESASHIOP,Intercept] 0.87 0.00 0.79 -0.62 0.34 0.84
## r_Site_ID[HEIWAOP,Intercept] 0.87 0.00 0.79 -0.63 0.35 0.85
## r_Site_ID[HENTONAOP,Intercept] 0.33 0.00 0.91 -1.39 -0.27 0.30
## r_Site_ID[KATSUUDAKEFR,Intercept] 0.33 0.00 0.90 -1.39 -0.27 0.30
## r_Site_ID[KEMINFR,Intercept] -1.04 0.00 0.86 -2.70 -1.60 -1.05
## r_Site_ID[KURASHIKIOP,Intercept] -0.24 0.00 0.72 -1.62 -0.71 -0.25
## r_Site_ID[NAGOFR,Intercept] 0.87 0.00 0.79 -0.60 0.35 0.85
## r_Site_ID[NAKAGUSUKUFR,Intercept] 0.13 0.00 0.73 -1.26 -0.35 0.11
## r_Site_ID[OISTFR,Intercept] 0.33 0.00 0.90 -1.39 -0.28 0.30
## r_Site_ID[OISTOP,Intercept] 0.18 0.00 0.89 -1.51 -0.42 0.16
## r_Site_ID[OKUFR,Intercept] -0.22 0.00 0.87 -1.89 -0.80 -0.25
## r_Site_ID[OYAMAOP,Intercept] 0.88 0.00 0.79 -0.62 0.35 0.85
## r_Site_ID[SEFAFR,Intercept] 0.03 0.00 0.73 -1.36 -0.45 0.01
## r_Site_ID[SENBARUOP,Intercept] -5.68 0.01 1.15 -7.98 -6.42 -5.67
## r_Site_ID[SUEYOSHIFR,Intercept] 0.87 0.00 0.79 -0.62 0.35 0.85
## r_Site_ID[TAMAGUSUKUFR,Intercept] -0.43 0.00 0.72 -1.80 -0.90 -0.45
## r_Site_ID[TOUNANOP,Intercept] 0.87 0.00 0.79 -0.62 0.34 0.84
## r_Site_ID[UEHARAFR,Intercept] 0.55 0.00 0.76 -0.91 0.04 0.53
## r_Site_ID[YONAFR,Intercept] 0.33 0.00 0.90 -1.39 -0.27 0.30
## lprior -11.32 0.01 1.04 -13.60 -11.92 -11.25
## lp__ -0.69 0.07 8.10 -16.49 -6.16 -0.76
## 75% 97.5% n_eff Rhat
## b_Intercept 1.28 1.97 35567 1
## b_LanduseDeveloped 1.23 2.43 38487 1
## sd_Site_ID__Intercept 1.93 2.57 25720 1
## phi 29.20 85.29 8303 1
## r_Site_ID[CHATANOP,Intercept] 1.37 2.51 47014 1
## r_Site_ID[GESASHIOP,Intercept] 1.37 2.50 47589 1
## r_Site_ID[HEIWAOP,Intercept] 1.37 2.49 48947 1
## r_Site_ID[HENTONAOP,Intercept] 0.90 2.20 48481 1
## r_Site_ID[KATSUUDAKEFR,Intercept] 0.90 2.19 51005 1
## r_Site_ID[KEMINFR,Intercept] -0.49 0.71 46018 1
## r_Site_ID[KURASHIKIOP,Intercept] 0.22 1.24 47422 1
## r_Site_ID[NAGOFR,Intercept] 1.37 2.52 49150 1
## r_Site_ID[NAKAGUSUKUFR,Intercept] 0.59 1.62 47490 1
## r_Site_ID[OISTFR,Intercept] 0.90 2.20 50264 1
## r_Site_ID[OISTOP,Intercept] 0.74 2.03 48917 1
## r_Site_ID[OKUFR,Intercept] 0.33 1.57 49696 1
## r_Site_ID[OYAMAOP,Intercept] 1.38 2.51 48177 1
## r_Site_ID[SEFAFR,Intercept] 0.49 1.53 50427 1
## r_Site_ID[SENBARUOP,Intercept] -4.93 -3.44 22477 1
## r_Site_ID[SUEYOSHIFR,Intercept] 1.37 2.50 49378 1
## r_Site_ID[TAMAGUSUKUFR,Intercept] 0.02 1.04 46817 1
## r_Site_ID[TOUNANOP,Intercept] 1.37 2.49 48201 1
## r_Site_ID[UEHARAFR,Intercept] 1.03 2.12 49956 1
## r_Site_ID[YONAFR,Intercept] 0.90 2.19 50542 1
## lprior -10.63 -9.48 13130 1
## lp__ 4.67 15.55 13542 1
##
## Samples were drawn using NUTS(diag_e) at Mon Nov 14 13:46:32 2022.
## For each parameter, n_eff is a crude measure of effective sample size,
## and Rhat is the potential scale reduction factor on split chains (at
## convergence, Rhat=1).bayes_R2(mod_nonspatial_beta) # pseudo r-squared of model## Estimate Est.Error Q2.5 Q97.5
## R2 0.8553803 0.08716908 0.6421023 0.9718465# simulate data from 500 random draws of posterior and compare it to observed data
# the black line should run through the center of the blue lines
pp_check(mod_nonspatial_beta, ndraws = 500) + theme_cowplot()
## Spatial Autocorrelation test
bres <- residuals(mod_nonspatial_beta)[,"Estimate"]
d_mat = as.matrix(dist(df[,c("Lat", "Long")], diag=T, upper=T)) # make distance matrix
d_mat_inv <- 1/d_mat
d_mat_inv[which(d_mat_inv == Inf)] <- 0
Moran.I(bres, d_mat_inv) # non-significant = no spatial autocorrelation## $observed
## [1] -0.1500793
##
## $expected
## [1] -0.05263158
##
## $sd
## [1] 0.09605959
##
## $p.value
## [1] 0.3103677NDSI_Bio - no effect of land use
# get pre vs post data for comparison
df<-tidy.stability_AI %>%
filter(Index %in% "NDSI_Bio" & response_variable %in% 'Recov')
df<-df[complete.cases(df),]
# change levels for model
df$Landuse<-df$Landuse %>% parse_character() %>% parse_factor(levels = c('Forest','Developed'))
#df$site_id<-df$site_id %>% parse_character() %>% parse_factor(levels = c(df$site_id[order(df$PC1)]))
mod_nonspatial_beta <-
brm(data = df, family = Beta(),
Stability ~ 1 + Landuse + (1|Site_ID),
iter = 5e4, warmup = 5000, chains = 4, cores = 4, thin = 2,
seed = 666)## Compiling Stan program...## Start sampling## Warning: There were 14 divergent transitions after warmup. See
## https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## to find out why this is a problem and how to eliminate them.## Warning: Examine the pairs() plot to diagnose sampling problemsmod_nonspatial_beta <- add_criterion(mod_nonspatial_beta, "loo")## Warning: Found 17 observations with a pareto_k > 0.7 in model
## 'mod_nonspatial_beta'. It is recommended to set 'moment_match = TRUE' in order
## to perform moment matching for problematic observations.#check MCMC traces
mcmc_plot(mod_nonspatial_beta, type = "trace") + theme_cowplot()
#check for agreement of chains
mcmc_plot(mod_nonspatial_beta, type = "dens_overlay") + theme_cowplot()
# plot posterior estimates of fixed effects
mcmc_plot(mod_nonspatial_beta, type = "intervals",prob = 0.68, prob_outer = 0.95, variable = "^b_", regex = TRUE) + theme_cowplot()
## no effects ##
## Parameter fits and stats
mod_nonspatial_beta$fit # Rhat values closer to 1 and n_eff values > 1000 are ideal.## Inference for Stan model: 516dc2723972b0b4959c45f1ed5792b7.
## 4 chains, each with iter=50000; warmup=5000; thin=2;
## post-warmup draws per chain=22500, total post-warmup draws=90000.
##
## mean se_mean sd 2.5% 25% 50%
## b_Intercept 0.91 0.00 0.59 -0.26 0.53 0.91
## b_LanduseDeveloped 1.62 0.01 0.96 -0.25 1.00 1.61
## sd_Site_ID__Intercept 1.90 0.00 0.42 1.19 1.61 1.86
## phi 37.09 0.25 32.93 5.05 15.65 27.51
## r_Site_ID[CHATANOP,Intercept] 1.73 0.00 0.89 0.06 1.14 1.70
## r_Site_ID[GENKAOP,Intercept] 0.35 0.00 0.98 -1.48 -0.31 0.32
## r_Site_ID[GESASHIOP,Intercept] 0.76 0.00 0.78 -0.72 0.25 0.74
## r_Site_ID[HEIWAOP,Intercept] 1.73 0.00 0.89 0.06 1.13 1.70
## r_Site_ID[KATSUUDAKEFR,Intercept] 0.35 0.00 0.98 -1.47 -0.30 0.32
## r_Site_ID[KEMINFR,Intercept] 0.35 0.00 0.99 -1.49 -0.31 0.31
## r_Site_ID[KURASHIKIOP,Intercept] 0.46 0.00 0.76 -0.99 -0.04 0.44
## r_Site_ID[MANABIFR,Intercept] 0.35 0.00 0.98 -1.48 -0.31 0.31
## r_Site_ID[NAGOFR,Intercept] -6.17 0.01 1.20 -8.58 -6.96 -6.17
## r_Site_ID[NAKAGUSUKUFR,Intercept] 1.72 0.00 0.89 0.05 1.12 1.70
## r_Site_ID[OISTFR,Intercept] -0.64 0.00 0.93 -2.40 -1.26 -0.67
## r_Site_ID[OISTOP,Intercept] 0.35 0.00 0.98 -1.49 -0.31 0.31
## r_Site_ID[OKUFR,Intercept] 0.35 0.00 0.98 -1.48 -0.31 0.32
## r_Site_ID[SENBARUOP,Intercept] -0.33 0.00 0.73 -1.74 -0.81 -0.34
## r_Site_ID[SUEYOSHIFR,Intercept] -0.68 0.00 0.72 -2.09 -1.15 -0.69
## r_Site_ID[TAMAGUSUKUFR,Intercept] 0.43 0.00 0.76 -1.02 -0.07 0.41
## r_Site_ID[TOUNANOP,Intercept] 0.99 0.00 0.80 -0.52 0.45 0.96
## r_Site_ID[UEHARAFR,Intercept] 0.32 0.00 0.75 -1.12 -0.18 0.30
## r_Site_ID[YACHOOP,Intercept] -0.30 0.00 0.73 -1.71 -0.78 -0.31
## r_Site_ID[YONAFR,Intercept] -1.01 0.00 0.92 -2.75 -1.62 -1.04
## lprior -12.04 0.01 1.20 -14.62 -12.77 -11.99
## lp__ 7.05 0.07 8.27 -9.49 1.50 7.20
## 75% 97.5% n_eff Rhat
## b_Intercept 1.30 2.08 33381 1
## b_LanduseDeveloped 2.24 3.54 36152 1
## sd_Site_ID__Intercept 2.15 2.85 22197 1
## phi 47.23 127.07 17827 1
## r_Site_ID[CHATANOP,Intercept] 2.29 3.57 40609 1
## r_Site_ID[GENKAOP,Intercept] 0.96 2.41 51216 1
## r_Site_ID[GESASHIOP,Intercept] 1.26 2.37 43678 1
## r_Site_ID[HEIWAOP,Intercept] 2.29 3.58 39848 1
## r_Site_ID[KATSUUDAKEFR,Intercept] 0.97 2.37 49854 1
## r_Site_ID[KEMINFR,Intercept] 0.96 2.41 50344 1
## r_Site_ID[KURASHIKIOP,Intercept] 0.94 2.00 42665 1
## r_Site_ID[MANABIFR,Intercept] 0.96 2.40 50556 1
## r_Site_ID[NAGOFR,Intercept] -5.38 -3.81 23631 1
## r_Site_ID[NAKAGUSUKUFR,Intercept] 2.28 3.56 40630 1
## r_Site_ID[OISTFR,Intercept] -0.06 1.27 44830 1
## r_Site_ID[OISTOP,Intercept] 0.97 2.40 49403 1
## r_Site_ID[OKUFR,Intercept] 0.97 2.40 49401 1
## r_Site_ID[SENBARUOP,Intercept] 0.13 1.13 41083 1
## r_Site_ID[SUEYOSHIFR,Intercept] -0.22 0.77 40480 1
## r_Site_ID[TAMAGUSUKUFR,Intercept] 0.91 1.97 41933 1
## r_Site_ID[TOUNANOP,Intercept] 1.49 2.62 42276 1
## r_Site_ID[UEHARAFR,Intercept] 0.79 1.86 42835 1
## r_Site_ID[YACHOOP,Intercept] 0.16 1.17 41732 1
## r_Site_ID[YONAFR,Intercept] -0.43 0.88 41219 1
## lprior -11.24 -9.83 16284 1
## lp__ 12.75 22.77 14012 1
##
## Samples were drawn using NUTS(diag_e) at Mon Nov 14 13:47:40 2022.
## For each parameter, n_eff is a crude measure of effective sample size,
## and Rhat is the potential scale reduction factor on split chains (at
## convergence, Rhat=1).bayes_R2(mod_nonspatial_beta) # pseudo r-squared of model## Estimate Est.Error Q2.5 Q97.5
## R2 0.9189229 0.06547899 0.7403166 0.9877987# simulate data from 500 random draws of posterior and compare it to observed data
# the black line should run through the center of the blue lines
pp_check(mod_nonspatial_beta, ndraws = 500) + theme_cowplot()
## Spatial Autocorrelation test
bres <- residuals(mod_nonspatial_beta)[,"Estimate"]
d_mat = as.matrix(dist(df[,c("Lat", "Long")], diag=T, upper=T)) # make distance matrix
d_mat_inv <- 1/d_mat
d_mat_inv[which(d_mat_inv == Inf)] <- 0
Moran.I(bres, d_mat_inv) # non-significant = no spatial autocorrelation## $observed
## [1] -0.09784212
##
## $expected
## [1] -0.05263158
##
## $sd
## [1] 0.1062802
##
## $p.value
## [1] 0.6705522NDSI_Anthro - no effect of land use
# get pre vs post data for comparison
df<-tidy.stability_AI %>%
filter(Index %in% "NDSI_Anth" & response_variable %in% 'Recov')
df<-df[complete.cases(df),]
# change levels for model
df$Landuse<-df$Landuse %>% parse_character() %>% parse_factor(levels = c('Forest','Developed'))
#df$site_id<-df$site_id %>% parse_character() %>% parse_factor(levels = c(df$site_id[order(df$PC1)]))
# fit random intercept and slope model because random slope model didn't converge well.
mod_nonspatial_beta <-
brm(data = df, family = Beta(),
Stability ~ 1 + Landuse + (1+Landuse|Site_ID),
iter = 5e4, warmup = 5000, chains = 4, cores = 4, thin = 2,
seed = 666)## Compiling Stan program...## Trying to compile a simple C file## Running /Library/Frameworks/R.framework/Resources/bin/R CMD SHLIB foo.c
## clang -mmacosx-version-min=10.13 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/Rcpp/include/" -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/" -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/unsupported" -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/BH/include" -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/StanHeaders/include/src/" -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/StanHeaders/include/" -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppParallel/include/" -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/rstan/include" -DEIGEN_NO_DEBUG -DBOOST_DISABLE_ASSERTS -DBOOST_PENDING_INTEGER_LOG2_HPP -DSTAN_THREADS -DBOOST_NO_AUTO_PTR -include '/Library/Frameworks/R.framework/Versions/4.2/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp' -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1 -I/usr/local/include -fPIC -Wall -g -O2 -c foo.c -o foo.o
## In file included from <built-in>:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.2/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
## In file included from /Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/Eigen/Dense:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/Eigen/Core:88:
## /Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:628:1: error: unknown type name 'namespace'
## namespace Eigen {
## ^
## /Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:628:16: error: expected ';' after top level declarator
## namespace Eigen {
## ^
## ;
## In file included from <built-in>:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.2/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
## In file included from /Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/Eigen/Dense:1:
## /Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/Eigen/Core:96:10: fatal error: 'complex' file not found
## #include <complex>
## ^~~~~~~~~
## 3 errors generated.
## make: *** [foo.o] Error 1## Start sampling## Warning: There were 1269 divergent transitions after warmup. See
## https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## to find out why this is a problem and how to eliminate them.## Warning: Examine the pairs() plot to diagnose sampling problemsmod_nonspatial_beta <- add_criterion(mod_nonspatial_beta, "loo")## Warning: Found 19 observations with a pareto_k > 0.7 in model
## 'mod_nonspatial_beta'. It is recommended to set 'moment_match = TRUE' in order
## to perform moment matching for problematic observations.#check MCMC traces
mcmc_plot(mod_nonspatial_beta, type = "trace") + theme_cowplot()
#check for agreement of chains
mcmc_plot(mod_nonspatial_beta, type = "dens_overlay") + theme_cowplot()
# plot posterior estimates of fixed effects
mcmc_plot(mod_nonspatial_beta, type = "intervals",prob = 0.68, prob_outer = 0.95, variable = "^b_", regex = TRUE) + theme_cowplot()
## no effects ##
## Parameter fits and stats
mod_nonspatial_beta$fit # Rhat values closer to 1 and n_eff values > 1000 are ideal.## Inference for Stan model: 92ef50f012c07027fd18c0f0569e8673.
## 4 chains, each with iter=50000; warmup=5000; thin=2;
## post-warmup draws per chain=22500, total post-warmup draws=90000.
##
## mean se_mean sd 2.5% 25%
## b_Intercept 0.04 0.00 0.53 -1.01 -0.30
## b_LanduseDeveloped 1.11 0.00 0.64 -0.15 0.70
## sd_Site_ID__Intercept 1.77 0.00 0.42 1.08 1.48
## sd_Site_ID__LanduseDeveloped 1.56 0.01 0.66 0.20 1.15
## cor_Site_ID__Intercept__LanduseDeveloped -0.86 0.00 0.28 -1.00 -0.99
## phi 31.22 0.57 29.32 4.90 12.94
## r_Site_ID[CHATANOP,Intercept] 0.52 0.00 0.69 -0.84 0.07
## r_Site_ID[GESASHIOP,Intercept] 0.83 0.00 0.70 -0.55 0.37
## r_Site_ID[HEIWAOP,Intercept] 1.26 0.00 0.73 -0.15 0.78
## r_Site_ID[HENTONAOP,Intercept] -0.07 0.01 1.60 -3.30 -1.03
## r_Site_ID[KATSUUDAKEFR,Intercept] -0.16 0.01 1.61 -3.42 -1.15
## r_Site_ID[KEMINFR,Intercept] 0.14 0.01 1.60 -3.15 -0.82
## r_Site_ID[KURASHIKIOP,Intercept] -1.23 0.00 0.72 -2.70 -1.68
## r_Site_ID[MANABIFR,Intercept] -0.37 0.02 1.77 -3.94 -1.45
## r_Site_ID[NAGOFR,Intercept] 0.10 0.00 0.68 -1.24 -0.34
## r_Site_ID[NAKAGUSUKUFR,Intercept] -1.12 0.00 0.72 -2.57 -1.57
## r_Site_ID[OISTFR,Intercept] 0.07 0.01 1.62 -3.23 -0.90
## r_Site_ID[OISTOP,Intercept] 0.14 0.01 1.59 -3.11 -0.83
## r_Site_ID[OKUFR,Intercept] 0.16 0.02 1.63 -3.11 -0.83
## r_Site_ID[OYAMAOP,Intercept] -0.26 0.00 0.69 -1.63 -0.70
## r_Site_ID[SEFAFR,Intercept] 1.26 0.00 0.73 -0.17 0.78
## r_Site_ID[SENBARUOP,Intercept] 0.85 0.00 0.71 -0.55 0.40
## r_Site_ID[SUEYOSHIFR,Intercept] 0.60 0.00 0.70 -0.78 0.15
## r_Site_ID[TAMAGUSUKUFR,Intercept] -5.14 0.01 1.13 -7.45 -5.88
## r_Site_ID[TOUNANOP,Intercept] 1.26 0.00 0.74 -0.17 0.78
## r_Site_ID[UEHARAFR,Intercept] 1.26 0.00 0.73 -0.17 0.78
## r_Site_ID[YONAFR,Intercept] 0.13 0.01 1.60 -3.18 -0.84
## r_Site_ID[CHATANOP,LanduseDeveloped] -0.41 0.01 0.83 -2.17 -0.89
## r_Site_ID[GESASHIOP,LanduseDeveloped] -0.67 0.01 0.86 -2.47 -1.19
## r_Site_ID[HEIWAOP,LanduseDeveloped] -1.02 0.01 0.94 -2.98 -1.60
## r_Site_ID[HENTONAOP,LanduseDeveloped] 0.00 0.01 1.55 -3.22 -0.86
## r_Site_ID[KATSUUDAKEFR,LanduseDeveloped] -0.01 0.01 1.56 -3.38 -0.86
## r_Site_ID[KEMINFR,LanduseDeveloped] 0.02 0.01 1.56 -3.12 -0.86
## r_Site_ID[KURASHIKIOP,LanduseDeveloped] 0.99 0.01 0.94 -0.68 0.36
## r_Site_ID[MANABIFR,LanduseDeveloped] -0.05 0.02 1.72 -3.80 -0.96
## r_Site_ID[NAGOFR,LanduseDeveloped] -0.08 0.01 0.80 -1.72 -0.53
## r_Site_ID[NAKAGUSUKUFR,LanduseDeveloped] 0.90 0.01 0.91 -0.75 0.29
## r_Site_ID[OISTFR,LanduseDeveloped] 0.02 0.01 1.57 -3.19 -0.84
## r_Site_ID[OISTOP,LanduseDeveloped] 0.01 0.01 1.54 -3.10 -0.86
## r_Site_ID[OKUFR,LanduseDeveloped] -0.01 0.02 1.58 -3.29 -0.88
## r_Site_ID[OYAMAOP,LanduseDeveloped] 0.21 0.00 0.81 -1.39 -0.25
## r_Site_ID[SEFAFR,LanduseDeveloped] -1.01 0.01 0.94 -2.96 -1.59
## r_Site_ID[SENBARUOP,LanduseDeveloped] -0.68 0.01 0.87 -2.52 -1.20
## r_Site_ID[SUEYOSHIFR,LanduseDeveloped] -0.48 0.01 0.84 -2.26 -0.97
## r_Site_ID[TAMAGUSUKUFR,LanduseDeveloped] 4.15 0.02 2.11 -0.14 2.87
## r_Site_ID[TOUNANOP,LanduseDeveloped] -1.02 0.01 0.94 -2.98 -1.60
## r_Site_ID[UEHARAFR,LanduseDeveloped] -1.01 0.01 0.94 -2.98 -1.58
## r_Site_ID[YONAFR,LanduseDeveloped] 0.02 0.01 1.56 -3.13 -0.85
## lprior -13.73 0.02 1.20 -16.44 -14.44
## lp__ -35.21 0.10 9.01 -52.95 -41.25
## 50% 75% 97.5% n_eff Rhat
## b_Intercept 0.04 0.37 1.07 14888 1
## b_LanduseDeveloped 1.11 1.52 2.40 17031 1
## sd_Site_ID__Intercept 1.73 2.02 2.72 13184 1
## sd_Site_ID__LanduseDeveloped 1.58 1.99 2.85 17236 1
## cor_Site_ID__Intercept__LanduseDeveloped -0.96 -0.88 0.13 35616 1
## phi 22.15 38.48 115.31 2679 1
## r_Site_ID[CHATANOP,Intercept] 0.52 0.96 1.91 22534 1
## r_Site_ID[GESASHIOP,Intercept] 0.83 1.28 2.25 23546 1
## r_Site_ID[HEIWAOP,Intercept] 1.25 1.73 2.74 23841 1
## r_Site_ID[HENTONAOP,Intercept] -0.06 0.90 3.15 15231 1
## r_Site_ID[KATSUUDAKEFR,Intercept] -0.18 0.82 3.17 22707 1
## r_Site_ID[KEMINFR,Intercept] 0.14 1.12 3.35 24130 1
## r_Site_ID[KURASHIKIOP,Intercept] -1.22 -0.76 0.18 25635 1
## r_Site_ID[MANABIFR,Intercept] -0.41 0.69 3.28 11753 1
## r_Site_ID[NAGOFR,Intercept] 0.10 0.53 1.45 23936 1
## r_Site_ID[NAKAGUSUKUFR,Intercept] -1.11 -0.66 0.27 23677 1
## r_Site_ID[OISTFR,Intercept] 0.07 1.03 3.33 11889 1
## r_Site_ID[OISTOP,Intercept] 0.15 1.12 3.34 26850 1
## r_Site_ID[OKUFR,Intercept] 0.15 1.14 3.53 10302 1
## r_Site_ID[OYAMAOP,Intercept] -0.25 0.18 1.10 23926 1
## r_Site_ID[SEFAFR,Intercept] 1.25 1.73 2.73 22160 1
## r_Site_ID[SENBARUOP,Intercept] 0.85 1.30 2.28 22880 1
## r_Site_ID[SUEYOSHIFR,Intercept] 0.60 1.04 1.99 24233 1
## r_Site_ID[TAMAGUSUKUFR,Intercept] -5.11 -4.37 -3.01 9882 1
## r_Site_ID[TOUNANOP,Intercept] 1.25 1.73 2.73 23241 1
## r_Site_ID[UEHARAFR,Intercept] 1.25 1.73 2.74 25088 1
## r_Site_ID[YONAFR,Intercept] 0.14 1.11 3.37 19028 1
## r_Site_ID[CHATANOP,LanduseDeveloped] -0.36 0.07 1.18 25237 1
## r_Site_ID[GESASHIOP,LanduseDeveloped] -0.62 -0.10 0.94 25750 1
## r_Site_ID[HEIWAOP,LanduseDeveloped] -0.98 -0.38 0.66 23565 1
## r_Site_ID[HENTONAOP,LanduseDeveloped] 0.00 0.85 3.24 14271 1
## r_Site_ID[KATSUUDAKEFR,LanduseDeveloped] 0.02 0.87 3.15 21848 1
## r_Site_ID[KEMINFR,LanduseDeveloped] -0.01 0.85 3.36 22039 1
## r_Site_ID[KURASHIKIOP,LanduseDeveloped] 0.94 1.55 2.96 24185 1
## r_Site_ID[MANABIFR,LanduseDeveloped] 0.02 0.93 3.36 11061 1
## r_Site_ID[NAGOFR,LanduseDeveloped] -0.06 0.37 1.52 24784 1
## r_Site_ID[NAKAGUSUKUFR,LanduseDeveloped] 0.86 1.44 2.80 31509 1
## r_Site_ID[OISTFR,LanduseDeveloped] 0.00 0.85 3.34 11750 1
## r_Site_ID[OISTOP,LanduseDeveloped] -0.02 0.84 3.30 23740 1
## r_Site_ID[OKUFR,LanduseDeveloped] -0.02 0.85 3.29 10179 1
## r_Site_ID[OYAMAOP,LanduseDeveloped] 0.17 0.67 1.88 28569 1
## r_Site_ID[SEFAFR,LanduseDeveloped] -0.97 -0.37 0.66 23434 1
## r_Site_ID[SENBARUOP,LanduseDeveloped] -0.64 -0.11 0.93 23163 1
## r_Site_ID[SUEYOSHIFR,LanduseDeveloped] -0.43 0.02 1.12 23027 1
## r_Site_ID[TAMAGUSUKUFR,LanduseDeveloped] 4.34 5.57 7.97 16437 1
## r_Site_ID[TOUNANOP,LanduseDeveloped] -0.98 -0.38 0.68 25148 1
## r_Site_ID[UEHARAFR,LanduseDeveloped] -0.97 -0.38 0.68 27877 1
## r_Site_ID[YONAFR,LanduseDeveloped] -0.01 0.86 3.39 18193 1
## lprior -13.62 -12.90 -11.67 4691 1
## lp__ -35.21 -29.13 -17.65 7718 1
##
## Samples were drawn using NUTS(diag_e) at Mon Nov 14 13:49:42 2022.
## For each parameter, n_eff is a crude measure of effective sample size,
## and Rhat is the potential scale reduction factor on split chains (at
## convergence, Rhat=1).bayes_R2(mod_nonspatial_beta) # pseudo r-squared of model## Estimate Est.Error Q2.5 Q97.5
## R2 0.8693717 0.08974146 0.6334231 0.9772584# simulate data from 500 random draws of posterior and compare it to observed data
# the black line should run through the center of the blue lines
pp_check(mod_nonspatial_beta, ndraws = 500) + theme_cowplot()
## Spatial Autocorrelation test
bres <- residuals(mod_nonspatial_beta)[,"Estimate"]
d_mat = as.matrix(dist(df[,c("Lat", "Long")], diag=T, upper=T)) # make distance matrix
d_mat_inv <- 1/d_mat
d_mat_inv[which(d_mat_inv == Inf)] <- 0
Moran.I(bres, d_mat_inv) # non-significant = no spatial autocorrelation## $observed
## [1] -0.1478582
##
## $expected
## [1] -0.05
##
## $sd
## [1] 0.09678781
##
## $p.value
## [1] 0.3119882acoustic index spatial variability
Not using same Bayesian models for spatial variability here. Instead, using break-point models to see if spatial variability changes after typhoons, and whether values and value change differ by land use. NB: set break-points manually before and after typhoons, as this is a priori expectation for break point locations (then compare model performance against linear model without breaks).
NDSI - typhoon x landuse interaction
# prepare data
df<-tidy.spatial_AI %>%
filter(Index %in% "NDSI" & response_group %in% 'Total_var') # get pre vs post data for comparison
df2<-df
df2$Date_Time<-df2$Date_Time %>% julian.Date() %>% as.numeric() # convert to numeric date format for model
df2$Date_Time<-df2$Date_Time - min(df2$Date_Time) # scale date to start at zero for model
#hist(df$Stability) # looks Gaussian
olm<-lm(Stability ~ 0 + Date_Time,data = df2) # linear model
summary(olm)##
## Call:
## lm(formula = Stability ~ 0 + Date_Time, data = df2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.12071 -0.01316 0.02577 0.09152 0.17317
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## Date_Time 5.993e-08 4.278e-10 140.1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.07568 on 3071 degrees of freedom
## Multiple R-squared: 0.8647, Adjusted R-squared: 0.8646
## F-statistic: 1.962e+04 on 1 and 3071 DF, p-value: < 2.2e-16#plot(olm)
## break-point model ##
os <- segmented(olm,
seg.Z = ~ Date_Time, # which variables to break
psi = c(df$Date_Time[df$Period %in% 'Trami'][1] %>% julian.Date() %>% as.numeric() -
df$Date_Time[1] %>% julian.Date() %>% as.numeric(), # break at Trami
df$Date_Time[df$Period %in% 'Post-typhoon'][1] %>% julian.Date() %>% as.numeric() -
df$Date_Time[1] %>% julian.Date() %>% as.numeric()), # break after typhoons
control = seg.control(it.max = 0)) # it.max = 0 prevents bootstrap procedure (i.e. forces manual break-point)
AIC(os,olm) # break point model performs better## df AIC
## os 4 -8353.921
## olm 2 -7138.036## davies test of the change in slope after break points
davies.test(olm,values = c(df$Date_Time[df$Period %in% 'Trami'][1] %>% julian.Date() %>% as.numeric() -
df$Date_Time[1] %>% julian.Date() %>% as.numeric(), # break at Trami
df$Date_Time[df$Period %in% 'Post-typhoon'][1] %>% julian.Date() %>% as.numeric() -
df$Date_Time[1] %>% julian.Date() %>% as.numeric())) # break after typhoons##
## Davies' test for a change in the slope
##
## data: formula = Stability ~ 0 + Date_Time , method = lm
## model = gaussian , link = identity
## segmented variable = Date_Time
## 'best' at = 2462400, n.points = 2, p-value < 2.2e-16
## alternative hypothesis: two.sidedsummary(os)##
## Call:
## lm(formula = Stability ~ Date_Time + U1.Date_Time + U2.Date_Time -
## 1, data = mfExt)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.091947 -0.014738 0.006873 0.048888 0.172848
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## Date_Time 9.592e-08 1.064e-09 90.13 <2e-16 ***
## U1.Date_Time -1.617e-07 7.642e-09 -21.16 <2e-16 ***
## U2.Date_Time 9.797e-08 8.397e-09 11.67 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.06207 on 3069 degrees of freedom
## Multiple R-squared: 0.909, Adjusted R-squared: 0.9089
## F-statistic: 1.022e+04 on 3 and 3069 DF, p-value: < 2.2e-16# get dates of manual breakpoint
Psi_1<-df$Date_Time[df$Period %in% 'Trami'][1]
Psi_2<-df$Date_Time[df$Period %in% 'Post-typhoon'][1]
# compare pre- and post-break acoustic index values
Break_results<-df %>%
filter(Period %in% c('Pre-typhoon','Post-typhoon')) %>%
group_by(Period) %>%
summarise(Med=median(Stability),
Lo_95=quantile(Stability,probs = 0.05),
Up_95=quantile(Stability,probs = 0.95))
Break_results$Period<-Break_results$Period %>% as.character() %>% parse_factor(levels = c('Pre-typhoon','Post-typhoon'))Plot results:
plot1<-df %>% ggplot(aes(x = Date_Time,
y = Stability)) +
geom_vline(xintercept = df$Date_Time[df$Period %in% 'Trami'][1],lty = 2) +
geom_vline(xintercept = df$Date_Time[df$Period %in% 'Post-typhoon'][1],lty = 2) +
geom_line(show.legend = F) +
labs(x = "Time",
y = "NDSI spatial variability") +
theme_cowplot(12) +
ylim(c(df$Stability %>% min(),
df$Stability %>% max()))
plot2<-Break_results %>% ggplot(aes(x = Period,
y = Med,
ymin = Lo_95,
ymax = Up_95,
shape = Period)) +
geom_pointrange(fatten = 5,
show.legend = F) +
labs(x = "Time period",
y = element_blank()) +
theme_cowplot(12) +
ylim(c(df$Stability %>% min(),
df$Stability %>% max()))
panel_a<-plot1 + plot2 + plot_layout(widths = c(3,1))
panel_a
Now check for landuse effect:
# prepare data
df<-tidy.spatial_AI %>%
filter(Index %in% "NDSI" & response_group %in% c('Forest_Var','Developed_Var')) # get pre vs post data for comparison
df$response_group[df$response_group %in% 'Forest_Var']<-'Forest'
df$response_group[df$response_group %in% 'Developed_Var']<-'Developed'
colnames(df)[4]<-'Landuse'
df$Landuse<-df$Landuse %>% as.character() %>% parse_factor(levels = c('Forest','Developed')) # change levels for model
df2<-df
df2$Date_Time<-df2$Date_Time %>% julian.Date() %>% as.numeric() # convert to numeric date format for model
df2$Date_Time<-df2$Date_Time - min(df2$Date_Time) # scale date to start at zero for model
# split out time series for each land use class separately
temp<-model.matrix(~ 0 + df2$Landuse)*df2$Date_Time
date_for<-temp[,1]
date_dev<-temp[,2]
rm(temp)
#hist(df$Stability) # looks Gaussian
olm<-lm(Stability ~ 0 + Landuse + date_for + date_dev,data = df2) # linear model
## break-point model ##
os <- segmented(olm,
seg.Z = ~ date_for + date_dev, # which variables to break
psi = list(date_for=c(df$Date_Time[df$Period %in% 'Trami'][1] %>% julian.Date() %>% as.numeric() -
df$Date_Time[1] %>% julian.Date() %>% as.numeric(), # break at Trami
df$Date_Time[df$Period %in% 'Post-typhoon'][1] %>% julian.Date() %>% as.numeric() -
df$Date_Time[1] %>% julian.Date() %>% as.numeric()), # break after typhoons
date_dev=c(df$Date_Time[df$Period %in% 'Trami'][1] %>% julian.Date() %>% as.numeric() -
df$Date_Time[1] %>% julian.Date() %>% as.numeric(), # break at Trami
df$Date_Time[df$Period %in% 'Post-typhoon'][1] %>% julian.Date() %>% as.numeric() -
df$Date_Time[1] %>% julian.Date() %>% as.numeric())), # break after typhoons
control = seg.control(it.max = 0)) # it.max = 0 prevents bootstrap procedure (i.e. forces manual break-point)
AIC(os,olm) # break point model performs better## df AIC
## os 9 -25599.50
## olm 5 -24645.79## davies test of the change in slope after break points
davies.test(olm,seg.Z = ~ date_for,
values = c(df$Date_Time[df$Period %in% 'Trami'][1] %>% julian.Date() %>% as.numeric() -
df$Date_Time[1] %>% julian.Date() %>% as.numeric(),
df$Date_Time[df$Period %in% 'Post-typhoon'][1] %>% julian.Date() %>% as.numeric() -
df$Date_Time[1] %>% julian.Date() %>% as.numeric())) # test for change in slope of forest sites##
## Davies' test for a change in the slope
##
## data: formula = Stability ~ 0 + Landuse + date_for + date_dev , method = lm
## model = gaussian , link = identity
## segmented variable = date_for
## 'best' at = 2462400, n.points = 2, p-value < 2.2e-16
## alternative hypothesis: two.sideddavies.test(olm,seg.Z = ~ date_dev,
values = c(df$Date_Time[df$Period %in% 'Trami'][1] %>% julian.Date() %>% as.numeric() -
df$Date_Time[1] %>% julian.Date() %>% as.numeric(),
df$Date_Time[df$Period %in% 'Post-typhoon'][1] %>% julian.Date() %>% as.numeric() -
df$Date_Time[1] %>% julian.Date() %>% as.numeric())) # test for change in slope of developed sites##
## Davies' test for a change in the slope
##
## data: formula = Stability ~ 0 + Landuse + date_for + date_dev , method = lm
## model = gaussian , link = identity
## segmented variable = date_dev
## 'best' at = 2462400, n.points = 2, p-value < 2.2e-16
## alternative hypothesis: two.sidedsummary(os)##
## Call:
## lm(formula = Stability ~ Landuse + date_for + date_dev + U1.date_for +
## U2.date_for + U1.date_dev + U2.date_dev - 1, data = mfExt)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.092178 -0.017600 0.000435 0.016101 0.129808
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## LanduseForest 2.017e-01 1.578e-03 127.787 < 2e-16 ***
## LanduseDeveloped 1.142e-01 1.578e-03 72.375 < 2e-16 ***
## date_for -7.566e-10 1.039e-09 -0.728 0.466372
## date_dev -5.820e-10 1.039e-09 -0.560 0.575302
## U1.date_for 5.013e-08 4.256e-09 11.778 < 2e-16 ***
## U2.date_for -1.493e-08 4.257e-09 -3.506 0.000458 ***
## U1.date_dev 1.757e-08 4.256e-09 4.129 3.7e-05 ***
## U2.date_dev 8.740e-09 4.257e-09 2.053 0.040115 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03011 on 6136 degrees of freedom
## Multiple R-squared: 0.9762, Adjusted R-squared: 0.9762
## F-statistic: 3.148e+04 on 8 and 6136 DF, p-value: < 2.2e-16# get dates of manual breakpoint
Psi_1<-df$Date_Time[df$Period %in% 'Trami'][1]
Psi_2<-df$Date_Time[df$Period %in% 'Post-typhoon'][1]
# compare pre- and post-break acoustic index values
Break_results<-df %>%
filter(Period %in% c('Pre-typhoon','Post-typhoon')) %>%
group_by(Landuse,Period) %>%
summarise(Med=median(Stability),
Lo_95=quantile(Stability,probs = 0.05),
Up_95=quantile(Stability,probs = 0.95))## `summarise()` has grouped output by 'Landuse'. You can override using the
## `.groups` argument.Break_results$Period<-Break_results$Period %>% as.character() %>% parse_factor(levels = c('Pre-typhoon','Post-typhoon'))
Break_results$Landuse<-Break_results$Landuse %>% as.character() %>% parse_factor(levels = c('Forest','Developed'))Plot results:
plot1<-df %>% ggplot(aes(x = Date_Time,
y = Stability,
col = Landuse)) +
scale_colour_manual(values = c("#29a62b","#a629a4")) +
geom_vline(xintercept = df$Date_Time[df$Period %in% 'Trami'][1],lty = 2) +
geom_vline(xintercept = df$Date_Time[df$Period %in% 'Post-typhoon'][1],lty = 2) +
geom_line(show.legend = F) +
labs(x = "Time",
y = "NDSI spatial variability") +
theme_cowplot(12) +
ylim(c(df$Stability %>% min(),
df$Stability %>% max()))
plot2<-Break_results %>% ggplot(aes(x = Landuse,
y = Med,
ymin = Lo_95,
ymax = Up_95,
col = Landuse,
shape = Period)) +
scale_colour_manual(values = c("#29a62b","#a629a4")) +
geom_pointrange(position = position_jitterdodge(),
fatten = 5,
show.legend = F) +
labs(x = "Landuse",
y = element_blank()) +
theme_cowplot(12) +
ylim(c(df$Stability %>% min(),
df$Stability %>% max()))
panel_b<-plot1 + plot2 + plot_layout(widths = c(3,1))
panel_b
panel_a / panel_b
For axis legend: “dashed lines delineate the pre- and post-typhoon periods.”
NDSI_Bio - Landuse x typhoon effect
# prepare data
df<-tidy.spatial_AI %>%
filter(Index %in% "NDSI_Bio" & response_group %in% 'Total_var') # get pre vs post data for comparison
df2<-df
df2$Date_Time<-df2$Date_Time %>% julian.Date() %>% as.numeric() # convert to numeric date format for model
df2$Date_Time<-df2$Date_Time - min(df2$Date_Time) # scale date to start at zero for model
#hist(df$Stability) # looks Gaussian
olm<-lm(Stability ~ 0 + Date_Time,data = df2) # linear model
summary(olm)##
## Call:
## lm(formula = Stability ~ 0 + Date_Time, data = df2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.15411 -0.01222 0.02325 0.09981 0.17069
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## Date_Time 7.834e-08 4.439e-10 176.5 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.07853 on 3071 degrees of freedom
## Multiple R-squared: 0.9103, Adjusted R-squared: 0.9102
## F-statistic: 3.115e+04 on 1 and 3071 DF, p-value: < 2.2e-16#plot(olm)
## break-point model ##
os <- segmented(olm,
seg.Z = ~ Date_Time, # which variables to break
psi = c(df$Date_Time[df$Period %in% 'Trami'][1] %>% julian.Date() %>% as.numeric() -
df$Date_Time[1] %>% julian.Date() %>% as.numeric(), # break at Trami
df$Date_Time[df$Period %in% 'Post-typhoon'][1] %>% julian.Date() %>% as.numeric() -
df$Date_Time[1] %>% julian.Date() %>% as.numeric()), # break after typhoons
control = seg.control(it.max = 0)) # it.max = 0 prevents bootstrap procedure (i.e. forces manual break-point)
AIC(os,olm) # break point model performs better## df AIC
## os 4 -7938.108
## olm 2 -6911.230## davies test of the change in slope after break points
davies.test(olm,values = c(df$Date_Time[df$Period %in% 'Trami'][1] %>% julian.Date() %>% as.numeric() -
df$Date_Time[1] %>% julian.Date() %>% as.numeric(), # break at Trami
df$Date_Time[df$Period %in% 'Post-typhoon'][1] %>% julian.Date() %>% as.numeric() -
df$Date_Time[1] %>% julian.Date() %>% as.numeric())) # break after typhoons##
## Davies' test for a change in the slope
##
## data: formula = Stability ~ 0 + Date_Time , method = lm
## model = gaussian , link = identity
## segmented variable = Date_Time
## 'best' at = 2462400, n.points = 2, p-value < 2.2e-16
## alternative hypothesis: two.sidedsummary(os)##
## Call:
## lm(formula = Stability ~ Date_Time + U1.Date_Time + U2.Date_Time -
## 1, data = mfExt)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.099923 -0.019365 0.008329 0.063828 0.169459
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## Date_Time 1.126e-07 1.139e-09 98.902 <2e-16 ***
## U1.Date_Time -1.474e-07 8.177e-09 -18.025 <2e-16 ***
## U2.Date_Time 8.354e-08 8.985e-09 9.298 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.06642 on 3069 degrees of freedom
## Multiple R-squared: 0.9358, Adjusted R-squared: 0.9358
## F-statistic: 1.492e+04 on 3 and 3069 DF, p-value: < 2.2e-16# get dates of manual breakpoint
Psi_1<-df$Date_Time[df$Period %in% 'Trami'][1]
Psi_2<-df$Date_Time[df$Period %in% 'Post-typhoon'][1]
# compare pre- and post-break acoustic index values
Break_results<-df %>%
filter(Period %in% c('Pre-typhoon','Post-typhoon')) %>%
group_by(Period) %>%
summarise(Med=median(Stability),
Lo_95=quantile(Stability,probs = 0.05),
Up_95=quantile(Stability,probs = 0.95))
Break_results$Period<-Break_results$Period %>% as.character() %>% parse_factor(levels = c('Pre-typhoon','Post-typhoon'))Plot results:
plot1<-df %>% ggplot(aes(x = Date_Time,
y = Stability)) +
geom_vline(xintercept = df$Date_Time[df$Period %in% 'Trami'][1],lty = 2) +
geom_vline(xintercept = df$Date_Time[df$Period %in% 'Post-typhoon'][1],lty = 2) +
geom_line(show.legend = F) +
labs(x = "Time",
y = expression(paste("NDSI"["Bio"]," spatial variability"))) +
theme_cowplot(12) +
ylim(c(df$Stability %>% min(),
df$Stability %>% max()))
plot2<-Break_results %>% ggplot(aes(x = Period,
y = Med,
ymin = Lo_95,
ymax = Up_95,
shape = Period)) +
geom_pointrange(fatten = 5,
show.legend = F) +
labs(x = "Time period",
y = element_blank()) +
theme_cowplot(12) +
ylim(c(df$Stability %>% min(),
df$Stability %>% max()))
panel_a<-plot1 + plot2 + plot_layout(widths = c(3,1))
panel_a
Now check for landuse effect:
# prepare data
df<-tidy.spatial_AI %>%
filter(Index %in% "NDSI_Bio" & response_group %in% c('Forest_Var','Developed_Var')) # get pre vs post data for comparison
df$response_group[df$response_group %in% 'Forest_Var']<-'Forest'
df$response_group[df$response_group %in% 'Developed_Var']<-'Developed'
colnames(df)[4]<-'Landuse'
df$Landuse<-df$Landuse %>% as.character() %>% parse_factor(levels = c('Forest','Developed')) # change levels for model
df2<-df
df2$Date_Time<-df2$Date_Time %>% julian.Date() %>% as.numeric() # convert to numeric date format for model
df2$Date_Time<-df2$Date_Time - min(df2$Date_Time) # scale date to start at zero for model
# split out time series for each land use class separately
temp<-model.matrix(~ 0 + df2$Landuse)*df2$Date_Time
date_for<-temp[,1]
date_dev<-temp[,2]
rm(temp)
#hist(df$Stability) # looks Gaussian
olm<-lm(Stability ~ 0 + Landuse + date_for + date_dev,data = df2) # linear model
## break-point model ##
os <- segmented(olm,
seg.Z = ~ date_for + date_dev, # which variables to break
psi = list(date_for=c(df$Date_Time[df$Period %in% 'Trami'][1] %>% julian.Date() %>% as.numeric() -
df$Date_Time[1] %>% julian.Date() %>% as.numeric(), # break at Trami
df$Date_Time[df$Period %in% 'Post-typhoon'][1] %>% julian.Date() %>% as.numeric() -
df$Date_Time[1] %>% julian.Date() %>% as.numeric()), # break after typhoons
date_dev=c(df$Date_Time[df$Period %in% 'Trami'][1] %>% julian.Date() %>% as.numeric() -
df$Date_Time[1] %>% julian.Date() %>% as.numeric(), # break at Trami
df$Date_Time[df$Period %in% 'Post-typhoon'][1] %>% julian.Date() %>% as.numeric() -
df$Date_Time[1] %>% julian.Date() %>% as.numeric())), # break after typhoons
control = seg.control(it.max = 0)) # it.max = 0 prevents bootstrap procedure (i.e. forces manual break-point)
AIC(os,olm) # break point model performs better## df AIC
## os 9 -23923.69
## olm 5 -22706.95## davies test of the change in slope after break points
davies.test(olm,seg.Z = ~ date_for,
values = c(df$Date_Time[df$Period %in% 'Trami'][1] %>% julian.Date() %>% as.numeric() -
df$Date_Time[1] %>% julian.Date() %>% as.numeric(),
df$Date_Time[df$Period %in% 'Post-typhoon'][1] %>% julian.Date() %>% as.numeric() -
df$Date_Time[1] %>% julian.Date() %>% as.numeric())) # test for change in slope of forest sites##
## Davies' test for a change in the slope
##
## data: formula = Stability ~ 0 + Landuse + date_for + date_dev , method = lm
## model = gaussian , link = identity
## segmented variable = date_for
## 'best' at = 2462400, n.points = 2, p-value < 2.2e-16
## alternative hypothesis: two.sideddavies.test(olm,seg.Z = ~ date_dev,
values = c(df$Date_Time[df$Period %in% 'Trami'][1] %>% julian.Date() %>% as.numeric() -
df$Date_Time[1] %>% julian.Date() %>% as.numeric(),
df$Date_Time[df$Period %in% 'Post-typhoon'][1] %>% julian.Date() %>% as.numeric() -
df$Date_Time[1] %>% julian.Date() %>% as.numeric())) # test for change in slope of developed sites##
## Davies' test for a change in the slope
##
## data: formula = Stability ~ 0 + Landuse + date_for + date_dev , method = lm
## model = gaussian , link = identity
## segmented variable = date_dev
## 'best' at = 2462400, n.points = 2, p-value < 2.2e-16
## alternative hypothesis: two.sidedsummary(os)##
## Call:
## lm(formula = Stability ~ Landuse + date_for + date_dev + U1.date_for +
## U2.date_for + U1.date_dev + U2.date_dev - 1, data = mfExt)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.121497 -0.022101 0.003447 0.022288 0.115851
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## LanduseForest 2.264e-01 1.809e-03 125.181 < 2e-16 ***
## LanduseDeveloped 9.354e-02 1.809e-03 51.713 < 2e-16 ***
## date_for 5.256e-09 1.190e-09 4.415 1.03e-05 ***
## date_dev 2.152e-08 1.190e-09 18.076 < 2e-16 ***
## U1.date_for 8.529e-08 4.878e-09 17.486 < 2e-16 ***
## U2.date_for -3.719e-08 4.879e-09 -7.622 2.88e-14 ***
## U1.date_dev 8.107e-08 4.878e-09 16.620 < 2e-16 ***
## U2.date_dev -6.912e-08 4.879e-09 -14.167 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0345 on 6136 degrees of freedom
## Multiple R-squared: 0.9818, Adjusted R-squared: 0.9818
## F-statistic: 4.138e+04 on 8 and 6136 DF, p-value: < 2.2e-16# get dates of manual breakpoint
Psi_1<-df$Date_Time[df$Period %in% 'Trami'][1]
Psi_2<-df$Date_Time[df$Period %in% 'Post-typhoon'][1]
# compare pre- and post-break acoustic index values
Break_results<-df %>%
filter(Period %in% c('Pre-typhoon','Post-typhoon')) %>%
group_by(Landuse,Period) %>%
summarise(Med=median(Stability),
Lo_95=quantile(Stability,probs = 0.05),
Up_95=quantile(Stability,probs = 0.95))## `summarise()` has grouped output by 'Landuse'. You can override using the
## `.groups` argument.Break_results$Period<-Break_results$Period %>% as.character() %>% parse_factor(levels = c('Pre-typhoon','Post-typhoon'))
Break_results$Landuse<-Break_results$Landuse %>% as.character() %>% parse_factor(levels = c('Forest','Developed'))Plot results:
plot1<-df %>% ggplot(aes(x = Date_Time,
y = Stability,
col = Landuse)) +
scale_colour_manual(values = c("#29a62b","#a629a4")) +
geom_vline(xintercept = df$Date_Time[df$Period %in% 'Trami'][1],lty = 2) +
geom_vline(xintercept = df$Date_Time[df$Period %in% 'Post-typhoon'][1],lty = 2) +
geom_line(show.legend = F) +
labs(x = "Time",
y = expression(paste("NDSI"["Bio"]," spatial variability"))) +
theme_cowplot(12) +
ylim(c(df$Stability %>% min(),
df$Stability %>% max()))
plot2<-Break_results %>% ggplot(aes(x = Landuse,
y = Med,
ymin = Lo_95,
ymax = Up_95,
col = Landuse,
shape = Period)) +
scale_colour_manual(values = c("#29a62b","#a629a4")) +
geom_pointrange(position = position_jitterdodge(),
fatten = 5,
show.legend = F) +
labs(x = "Landuse",
y = element_blank()) +
theme_cowplot(12) +
ylim(c(df$Stability %>% min(),
df$Stability %>% max()))
panel_b<-plot1 + plot2 + plot_layout(widths = c(3,1))
panel_b
panel_a / panel_b
NDSI_Anthro - no effect
# prepare data
df<-tidy.spatial_AI %>%
filter(Index %in% "NDSI_Anth" & response_group %in% 'Total_var') # get pre vs post data for comparison
df2<-df
df2$Date_Time<-df2$Date_Time %>% julian.Date() %>% as.numeric() # convert to numeric date format for model
df2$Date_Time<-df2$Date_Time - min(df2$Date_Time) # scale date to start at zero for model
#hist(df$Stability) # looks Gaussian
olm<-lm(Stability ~ 0 + Date_Time,data = df2) # linear model
summary(olm)##
## Call:
## lm(formula = Stability ~ 0 + Date_Time, data = df2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.41002 -0.09525 0.10893 0.46349 0.68911
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## Date_Time 1.323e-07 2.081e-09 63.59 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3681 on 3071 degrees of freedom
## Multiple R-squared: 0.5684, Adjusted R-squared: 0.5682
## F-statistic: 4044 on 1 and 3071 DF, p-value: < 2.2e-16#plot(olm)
## break-point model ##
os <- segmented(olm,
seg.Z = ~ Date_Time, # which variables to break
psi = c(df$Date_Time[df$Period %in% 'Trami'][1] %>% julian.Date() %>% as.numeric() -
df$Date_Time[1] %>% julian.Date() %>% as.numeric(), # break at Trami
df$Date_Time[df$Period %in% 'Post-typhoon'][1] %>% julian.Date() %>% as.numeric() -
df$Date_Time[1] %>% julian.Date() %>% as.numeric()), # break after typhoons
control = seg.control(it.max = 0)) # it.max = 0 prevents bootstrap procedure (i.e. forces manual break-point)
AIC(os,olm) # break point model performs better## df AIC
## os 4 848.6697
## olm 2 2580.0874## davies test of the change in slope after break points
davies.test(olm,values = c(df$Date_Time[df$Period %in% 'Trami'][1] %>% julian.Date() %>% as.numeric() -
df$Date_Time[1] %>% julian.Date() %>% as.numeric(), # break at Trami
df$Date_Time[df$Period %in% 'Post-typhoon'][1] %>% julian.Date() %>% as.numeric() -
df$Date_Time[1] %>% julian.Date() %>% as.numeric())) # break after typhoons##
## Davies' test for a change in the slope
##
## data: formula = Stability ~ 0 + Date_Time , method = lm
## model = gaussian , link = identity
## segmented variable = Date_Time
## 'best' at = 2462400, n.points = 2, p-value < 2.2e-16
## alternative hypothesis: two.sidedsummary(os)##
## Call:
## lm(formula = Stability ~ Date_Time + U1.Date_Time + U2.Date_Time -
## 1, data = mfExt)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.61843 -0.07874 0.07295 0.30630 0.57547
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## Date_Time 3.278e-07 4.759e-09 68.89 <2e-16 ***
## U1.Date_Time -8.166e-07 3.417e-08 -23.90 <2e-16 ***
## U2.Date_Time 4.407e-07 3.755e-08 11.73 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2776 on 3069 degrees of freedom
## Multiple R-squared: 0.7547, Adjusted R-squared: 0.7544
## F-statistic: 3147 on 3 and 3069 DF, p-value: < 2.2e-16# get dates of manual breakpoint
Psi_1<-df$Date_Time[df$Period %in% 'Trami'][1]
Psi_2<-df$Date_Time[df$Period %in% 'Post-typhoon'][1]
# compare pre- and post-break acoustic index values
Break_results<-df %>%
filter(Period %in% c('Pre-typhoon','Post-typhoon')) %>%
group_by(Period) %>%
summarise(Med=median(Stability),
Lo_95=quantile(Stability,probs = 0.05),
Up_95=quantile(Stability,probs = 0.95))
Break_results$Period<-Break_results$Period %>% as.character() %>% parse_factor(levels = c('Pre-typhoon','Post-typhoon'))Plot results:
plot1<-df %>% ggplot(aes(x = Date_Time,
y = Stability)) +
geom_vline(xintercept = df$Date_Time[df$Period %in% 'Trami'][1],lty = 2) +
geom_vline(xintercept = df$Date_Time[df$Period %in% 'Post-typhoon'][1],lty = 2) +
geom_line(show.legend = F) +
labs(x = "Time",
y = expression(paste("NDSI"["Anthro"]," spatial variability"))) +
theme_cowplot(12) +
ylim(c(df$Stability %>% min(),
df$Stability %>% max()))
plot2<-Break_results %>% ggplot(aes(x = Period,
y = Med,
ymin = Lo_95,
ymax = Up_95,
shape = Period)) +
geom_pointrange(fatten = 5,
show.legend = F) +
labs(x = "Time period",
y = element_blank()) +
theme_cowplot(12) +
ylim(c(df$Stability %>% min(),
df$Stability %>% max()))
panel_a<-plot1 + plot2 + plot_layout(widths = c(3,1))
panel_a
NB: model slopes differ slightly so Davies test is sig.
Now check for landuse effect:
# prepare data
df<-tidy.spatial_AI %>%
filter(Index %in% "NDSI_Anth" & response_group %in% c('Forest_Var','Developed_Var')) # get pre vs post data for comparison
df$response_group[df$response_group %in% 'Forest_Var']<-'Forest'
df$response_group[df$response_group %in% 'Developed_Var']<-'Developed'
colnames(df)[4]<-'Landuse'
df$Landuse<-df$Landuse %>% as.character() %>% parse_factor(levels = c('Forest','Developed')) # change levels for model
df2<-df
df2$Date_Time<-df2$Date_Time %>% julian.Date() %>% as.numeric() # convert to numeric date format for model
df2$Date_Time<-df2$Date_Time - min(df2$Date_Time) # scale date to start at zero for model
# split out time series for each land use class separately
temp<-model.matrix(~ 0 + df2$Landuse)*df2$Date_Time
date_for<-temp[,1]
date_dev<-temp[,2]
rm(temp)
#hist(df$Stability) # looks Gaussian
olm<-lm(Stability ~ 0 + Landuse + date_for + date_dev,data = df2) # linear model
## break-point model ##
os <- segmented(olm,
seg.Z = ~ date_for + date_dev, # which variables to break
psi = list(date_for=c(df$Date_Time[df$Period %in% 'Trami'][1] %>% julian.Date() %>% as.numeric() -
df$Date_Time[1] %>% julian.Date() %>% as.numeric(), # break at Trami
df$Date_Time[df$Period %in% 'Post-typhoon'][1] %>% julian.Date() %>% as.numeric() -
df$Date_Time[1] %>% julian.Date() %>% as.numeric()), # break after typhoons
date_dev=c(df$Date_Time[df$Period %in% 'Trami'][1] %>% julian.Date() %>% as.numeric() -
df$Date_Time[1] %>% julian.Date() %>% as.numeric(), # break at Trami
df$Date_Time[df$Period %in% 'Post-typhoon'][1] %>% julian.Date() %>% as.numeric() -
df$Date_Time[1] %>% julian.Date() %>% as.numeric())), # break after typhoons
control = seg.control(it.max = 0)) # it.max = 0 prevents bootstrap procedure (i.e. forces manual break-point)
AIC(os,olm) # break point model performs better## df AIC
## os 9 -5845.419
## olm 5 -5696.789## davies test of the change in slope after break points
davies.test(olm,seg.Z = ~ date_for,
values = c(df$Date_Time[df$Period %in% 'Trami'][1] %>% julian.Date() %>% as.numeric() -
df$Date_Time[1] %>% julian.Date() %>% as.numeric(),
df$Date_Time[df$Period %in% 'Post-typhoon'][1] %>% julian.Date() %>% as.numeric() -
df$Date_Time[1] %>% julian.Date() %>% as.numeric())) # test for change in slope of forest sites##
## Davies' test for a change in the slope
##
## data: formula = Stability ~ 0 + Landuse + date_for + date_dev , method = lm
## model = gaussian , link = identity
## segmented variable = date_for
## 'best' at = 2462400, n.points = 2, p-value = 1.274e-08
## alternative hypothesis: two.sideddavies.test(olm,seg.Z = ~ date_dev,
values = c(df$Date_Time[df$Period %in% 'Trami'][1] %>% julian.Date() %>% as.numeric() -
df$Date_Time[1] %>% julian.Date() %>% as.numeric(),
df$Date_Time[df$Period %in% 'Post-typhoon'][1] %>% julian.Date() %>% as.numeric() -
df$Date_Time[1] %>% julian.Date() %>% as.numeric())) # test for change in slope of developed sites##
## Davies' test for a change in the slope
##
## data: formula = Stability ~ 0 + Landuse + date_for + date_dev , method = lm
## model = gaussian , link = identity
## segmented variable = date_dev
## 'best' at = 2462400, n.points = 2, p-value < 2.2e-16
## alternative hypothesis: two.sidedsummary(os)##
## Call:
## lm(formula = Stability ~ Landuse + date_for + date_dev + U1.date_for +
## U2.date_for + U1.date_dev + U2.date_dev - 1, data = mfExt)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.3870 -0.1040 -0.0239 0.1164 0.3847
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## LanduseForest 5.857e-01 7.877e-03 74.363 < 2e-16 ***
## LanduseDeveloped 6.909e-01 7.877e-03 87.714 < 2e-16 ***
## date_for -6.227e-09 5.184e-09 -1.201 0.229741
## date_dev -1.004e-07 5.184e-09 -19.366 < 2e-16 ***
## U1.date_for -1.177e-07 2.124e-08 -5.542 3.11e-08 ***
## U2.date_for 8.035e-08 2.125e-08 3.782 0.000157 ***
## U1.date_dev 7.223e-08 2.124e-08 3.400 0.000677 ***
## U2.date_dev -4.929e-10 2.125e-08 -0.023 0.981492
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1503 on 6136 degrees of freedom
## Multiple R-squared: 0.9176, Adjusted R-squared: 0.9175
## F-statistic: 8540 on 8 and 6136 DF, p-value: < 2.2e-16# get dates of manual breakpoint
Psi_1<-df$Date_Time[df$Period %in% 'Trami'][1]
Psi_2<-df$Date_Time[df$Period %in% 'Post-typhoon'][1]
# compare pre- and post-break acoustic index values
Break_results<-df %>%
filter(Period %in% c('Pre-typhoon','Post-typhoon')) %>%
group_by(Landuse,Period) %>%
summarise(Med=median(Stability),
Lo_95=quantile(Stability,probs = 0.05),
Up_95=quantile(Stability,probs = 0.95))## `summarise()` has grouped output by 'Landuse'. You can override using the
## `.groups` argument.Break_results$Period<-Break_results$Period %>% as.character() %>% parse_factor(levels = c('Pre-typhoon','Post-typhoon'))
Break_results$Landuse<-Break_results$Landuse %>% as.character() %>% parse_factor(levels = c('Forest','Developed'))Plot results:
plot1<-df %>% ggplot(aes(x = Date_Time,
y = Stability,
col = Landuse)) +
scale_colour_manual(values = c("#29a62b","#a629a4")) +
geom_vline(xintercept = df$Date_Time[df$Period %in% 'Trami'][1],lty = 2) +
geom_vline(xintercept = df$Date_Time[df$Period %in% 'Post-typhoon'][1],lty = 2) +
geom_line(show.legend = F) +
labs(x = "Time",
y = expression(paste("NDSI"["Anthro"]," spatial variability"))) +
theme_cowplot(12) +
ylim(c(df$Stability %>% min(),
df$Stability %>% max()))
plot2<-Break_results %>% ggplot(aes(x = Landuse,
y = Med,
ymin = Lo_95,
ymax = Up_95,
col = Landuse,
shape = Period)) +
scale_colour_manual(values = c("#29a62b","#a629a4")) +
geom_pointrange(position = position_jitterdodge(),
fatten = 5,
show.legend = F) +
labs(x = "Landuse",
y = element_blank()) +
theme_cowplot(12) +
ylim(c(df$Stability %>% min(),
df$Stability %>% max()))
panel_b<-plot1 + plot2 + plot_layout(widths = c(3,1))
panel_b
panel_a / panel_b
NB: model slopes differ slightly so Davies test is sig.
NB: this is very reassuring - that what we’re seeing in the spatial variability results is biotic and independent of background noise. If results weren’t driven by site differences in biotic responses, we’d see them follow closely the overall trend like for Anthropophony.
Bird detections
NB: Bird detections were not made at every site, so the random site effect fills in the missing sites when constructing posterior draws etc. (we simply ignore those results from sites where detections were not observed).
bird detection mean states
Total detections - species x typhoon effect
dat_pre<-tidy.stability_bird %>%
filter(Cutoff %in% 0.5 & response_variable %in% 'Pre_mean')
dat_post<-tidy.stability_bird %>%
filter(Cutoff %in% 0.5 & response_variable %in% 'Post_mean')
dat_pre<-dat_pre[complete.cases(dat_pre),]
dat_post<-dat_post[complete.cases(dat_post),]
dat_pre$Typhoon = rep("Pre", nrow(dat_pre))
dat_post$Typhoon = rep("Post", nrow(dat_post))
df <- rbind(dat_pre, dat_post)
# change levels for model
df$Typhoon<-df$Typhoon %>% parse_character() %>% parse_factor(levels = c('Pre','Post'))
df$Landuse<-df$Landuse %>% parse_character() %>% parse_factor(levels = c('Forest','Developed'))
df$Stability<-df$Stability+1 # try this to run lognormal model (cannot have zero values)
# set weakly informative priors
all_priors<-c(
set_prior("normal(0, 2)",coef = "LanduseDeveloped"),
set_prior("normal(0, 2)",coef = "LanduseDeveloped:Species_IDHorornis_diphone"),
set_prior("normal(0, 2)",coef = "LanduseDeveloped:Species_IDOtus_elegans"),
set_prior("normal(0, 2)",coef = "LanduseDeveloped:TyphoonPost"),
set_prior("normal(0, 2)",coef = "Species_IDHorornis_diphone"),
set_prior("normal(0, 2)",coef = "Species_IDOtus_elegans"),
set_prior("normal(0, 2)",coef = "TyphoonPost"),
set_prior("normal(0, 2)",coef = "TyphoonPost:Species_IDHorornis_diphone"),
set_prior("normal(0, 2)",coef = "TyphoonPost:Species_IDOtus_elegans"))
# Fit bayesian mixed effects model
mod_nonspatial_log <-
brm(data = df, family = lognormal(),
round(Stability) ~ 1 + Landuse + Typhoon + Species_ID + # used round() as poisson needs integers
Landuse:Typhoon + Landuse:Species_ID + Typhoon:Species_ID + (1|Site_ID),
iter = 5e4, warmup = 5000, chains = 4, cores = 4, thin = 2,
seed = 666, prior = all_priors)## Compiling Stan program...## Trying to compile a simple C file## Running /Library/Frameworks/R.framework/Resources/bin/R CMD SHLIB foo.c
## clang -mmacosx-version-min=10.13 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/Rcpp/include/" -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/" -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/unsupported" -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/BH/include" -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/StanHeaders/include/src/" -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/StanHeaders/include/" -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppParallel/include/" -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/rstan/include" -DEIGEN_NO_DEBUG -DBOOST_DISABLE_ASSERTS -DBOOST_PENDING_INTEGER_LOG2_HPP -DSTAN_THREADS -DBOOST_NO_AUTO_PTR -include '/Library/Frameworks/R.framework/Versions/4.2/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp' -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1 -I/usr/local/include -fPIC -Wall -g -O2 -c foo.c -o foo.o
## In file included from <built-in>:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.2/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
## In file included from /Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/Eigen/Dense:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/Eigen/Core:88:
## /Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:628:1: error: unknown type name 'namespace'
## namespace Eigen {
## ^
## /Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:628:16: error: expected ';' after top level declarator
## namespace Eigen {
## ^
## ;
## In file included from <built-in>:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.2/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
## In file included from /Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/Eigen/Dense:1:
## /Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/Eigen/Core:96:10: fatal error: 'complex' file not found
## #include <complex>
## ^~~~~~~~~
## 3 errors generated.
## make: *** [foo.o] Error 1## Start samplingbrms::mcmc_plot(mod_nonspatial_log, type = "trace") + theme_cowplot()## No divergences to plot.
#check for agreement of chains
brms::mcmc_plot(mod_nonspatial_log, type = "dens_overlay") + theme_cowplot()
# plot posterior probabilities
brms::mcmc_plot(mod_nonspatial_log, type = "intervals",prob = 0.68, prob_outer = 0.95, variable = "^b_", regex = TRUE) + theme_cowplot()
pp_check(mod_nonspatial_log, ndraws = 500) + theme_cowplot() + scale_x_continuous(limits = c(0,50)) # good fit to data## Warning: Removed 9564 rows containing non-finite values (stat_density).## Warning: Removed 19 rows containing non-finite values (stat_density).
bayes_R2(mod_nonspatial_log)## Estimate Est.Error Q2.5 Q97.5
## R2 0.4735329 0.0931045 0.2723411 0.6284559bres <- residuals(mod_nonspatial_log)[,"Estimate"]
d_mat = as.matrix(dist(df[,c("Lat", "Long")], diag=T, upper=T))
d_mat_inv <- 1/d_mat
d_mat_inv[which(d_mat_inv == Inf)] <- 0
ape::Moran.I(bres, d_mat_inv)## $observed
## [1] 0.008690685
##
## $expected
## [1] -0.01123596
##
## $sd
## [1] 0.02164809
##
## $p.value
## [1] 0.3573219summary(mod_nonspatial_log)## Family: lognormal
## Links: mu = identity; sigma = identity
## Formula: round(Stability) ~ 1 + Landuse + Typhoon + Species_ID + Landuse:Typhoon + Landuse:Species_ID + Typhoon:Species_ID + (1 | Site_ID)
## Data: df (Number of observations: 90)
## Draws: 4 chains, each with iter = 50000; warmup = 5000; thin = 2;
## total post-warmup draws = 90000
##
## Group-Level Effects:
## ~Site_ID (Number of levels: 22)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.69 0.18 0.37 1.09 1.00 42601 55290
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI
## Intercept 3.57 0.32 2.94
## LanduseDeveloped 0.25 0.44 -0.62
## TyphoonPost -0.18 0.32 -0.81
## Species_IDHorornis_diphone -1.06 0.35 -1.76
## Species_IDOtus_elegans -0.46 1.43 -3.25
## LanduseDeveloped:TyphoonPost -0.41 0.38 -1.17
## LanduseDeveloped:Species_IDHorornis_diphone -0.32 0.41 -1.12
## LanduseDeveloped:Species_IDOtus_elegans -0.47 1.43 -3.26
## TyphoonPost:Species_IDHorornis_diphone -1.25 0.39 -2.01
## TyphoonPost:Species_IDOtus_elegans -0.13 0.55 -1.20
## u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 4.20 1.00 65820 77332
## LanduseDeveloped 1.12 1.00 60698 75892
## TyphoonPost 0.45 1.00 79070 81088
## Species_IDHorornis_diphone -0.36 1.00 74815 80432
## Species_IDOtus_elegans 2.34 1.00 80432 81997
## LanduseDeveloped:TyphoonPost 0.35 1.00 81738 83245
## LanduseDeveloped:Species_IDHorornis_diphone 0.49 1.00 77284 81097
## LanduseDeveloped:Species_IDOtus_elegans 2.31 1.00 80508 81350
## TyphoonPost:Species_IDHorornis_diphone -0.49 1.00 76040 80466
## TyphoonPost:Species_IDOtus_elegans 0.95 1.00 78671 80924
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 0.86 0.08 0.72 1.04 1.00 58350 70632
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).Plot main species effect:
# Horornis #
mut_mod<-mod_nonspatial_log %>%
spread_draws(b_Species_IDHorornis_diphone, r_Site_ID[Site_ID,]) %>%
mutate(site_mean = b_Species_IDHorornis_diphone + r_Site_ID) # get posterior distribution data for plotting
mut_mod$sig<-0
for (i in 1:length(unique(mut_mod$Site_ID))) {
temp<-mut_mod$site_mean[mut_mod$Site_ID %in% mut_mod$Site_ID[i]] %>%
quantile(c(0.05,0.95)) %>% # get 95% credible intervals
sign() %>% # get sign of those intervals
duplicated() # are the signs identical (no = zero-spanning)
if(temp[2] %in% TRUE){ # if signs are identical (non-zero-spanning)
mut_mod$sig[mut_mod$Site_ID %in% mut_mod$Site_ID[i]]<-1 # add significance indicator
}
} # determine whether credible intervals span zero, and store as binary (to show in plot):
rm(temp)
mut_mod$sig<-mut_mod$sig %>% as.character() %>% parse_factor(levels = c('0','1')) # convert to factor
horornisplot <- mut_mod %>%
ggplot(aes(y = Site_ID %>% as.character %>% parse_factor(levels = Site_order),
x = site_mean,
fill = stat(x) > 0,
shape = sig)) +
stat_halfeye(show.legend = F,slab_type = "pdf") +
geom_vline(xintercept = 0,
linetype = "dashed") +
scale_fill_manual(values = c("gray80","navajowhite")) +
scale_shape_manual(values = c(21,8)) +
labs(x = "Posterior draws (species effect)",
y = "") +
cowplot::theme_minimal_grid()
# Otus #
mut_mod<-mod_nonspatial_log %>%
spread_draws(b_Species_IDOtus_elegans, r_Site_ID[Site_ID,]) %>%
mutate(site_mean = b_Species_IDOtus_elegans + r_Site_ID) # get posterior distribution data for plotting
mut_mod$sig<-0
for (i in 1:length(unique(mut_mod$Site_ID))) {
temp<-mut_mod$site_mean[mut_mod$Site_ID %in% mut_mod$Site_ID[i]] %>%
quantile(c(0.05,0.95)) %>% # get 95% credible intervals
sign() %>% # get sign of those intervals
duplicated() # are the signs identical (no = zero-spanning)
if(temp[2] %in% TRUE){ # if signs are identical (non-zero-spanning)
mut_mod$sig[mut_mod$Site_ID %in% mut_mod$Site_ID[i]]<-1 # add significance indicator
}
} # determine whether credible intervals span zero, and store as binary (to show in plot):
rm(temp)
mut_mod$sig<-mut_mod$sig %>% as.character() %>% parse_factor(levels = c('0','1')) # convert to factor
otusplot <-mut_mod %>%
ggplot(aes(y = Site_ID %>% as.character %>% parse_factor(levels = Site_order),
x = site_mean,
fill = stat(x) > 0,
shape = sig)) +
stat_halfeye(show.legend = F,slab_type = "pdf") +
geom_vline(xintercept = 0,
linetype = "dashed") +
scale_fill_manual(values = c("gray80","navajowhite")) +
scale_shape_manual(values = c(21,8)) +
labs(x = "Posterior draws (species effect)",
y = "") +
cowplot::theme_minimal_grid()
# plots #
plot_grid(horornisplot, otusplot,
ncol = 2, align = "hv",
labels = c("Horonis", "Otus"))
Plot typhoon x species interaction:
# Corvus #
mut_mod<-mod_nonspatial_log %>%
spread_draws(b_TyphoonPost, r_Site_ID[Site_ID,]) %>%
mutate(site_mean = b_TyphoonPost + r_Site_ID) # get posterior distribution data for plotting
mut_mod$sig<-0
for (i in 1:length(unique(mut_mod$Site_ID))) {
temp<-mut_mod$site_mean[mut_mod$Site_ID %in% mut_mod$Site_ID[i]] %>%
quantile(c(0.05,0.95)) %>% # get 95% credible intervals
sign() %>% # get sign of those intervals
duplicated() # are the signs identical (no = zero-spanning)
if(temp[2] %in% TRUE){ # if signs are identical (non-zero-spanning)
mut_mod$sig[mut_mod$Site_ID %in% mut_mod$Site_ID[i]]<-1 # add significance indicator
}
} # determine whether credible intervals span zero, and store as binary (to show in plot):
rm(temp)
mut_mod$sig<-mut_mod$sig %>% as.character() %>% parse_factor(levels = c('0','1')) # convert to factor
corvusplot <- mut_mod %>%
ggplot(aes(y = Site_ID %>% as.character %>% parse_factor(levels = Site_order),
x = site_mean,
fill = stat(x) > 0,
shape = sig)) +
stat_halfeye(show.legend = F,slab_type = "pdf") +
geom_vline(xintercept = 0,
linetype = "dashed") +
scale_fill_manual(values = c("gray80","skyblue")) +
scale_shape_manual(values = c(21,8)) +
labs(x = "Posterior draws (typhoon effect)",
y = "Site") +
cowplot::theme_minimal_grid()
# Horornis #
mut_mod<-mod_nonspatial_log %>%
spread_draws(b_TyphoonPost, `b_TyphoonPost:Species_IDHorornis_diphone`, r_Site_ID[Site_ID,]) %>%
mutate(site_mean = b_TyphoonPost + `b_TyphoonPost:Species_IDHorornis_diphone` + r_Site_ID) # get posterior distribution data for plotting
mut_mod$sig<-0
for (i in 1:length(unique(mut_mod$Site_ID))) {
temp<-mut_mod$site_mean[mut_mod$Site_ID %in% mut_mod$Site_ID[i]] %>%
quantile(c(0.05,0.95)) %>% # get 95% credible intervals
sign() %>% # get sign of those intervals
duplicated() # are the signs identical (no = zero-spanning)
if(temp[2] %in% TRUE){ # if signs are identical (non-zero-spanning)
mut_mod$sig[mut_mod$Site_ID %in% mut_mod$Site_ID[i]]<-1 # add significance indicator
}
} # determine whether credible intervals span zero, and store as binary (to show in plot):
rm(temp)
mut_mod$sig<-mut_mod$sig %>% as.character() %>% parse_factor(levels = c('0','1')) # convert to factor
horornisplot <- mut_mod %>%
ggplot(aes(y = Site_ID %>% as.character %>% parse_factor(levels = Site_order),
x = site_mean,
fill = stat(x) > 0,
shape = sig)) +
stat_halfeye(show.legend = F,slab_type = "pdf") +
geom_vline(xintercept = 0,
linetype = "dashed") +
scale_fill_manual(values = c("gray80","skyblue")) +
scale_shape_manual(values = c(21,8)) +
labs(x = "Posterior draws (typhoon effect)",
y = "") +
cowplot::theme_minimal_grid()
# Otus #
mut_mod<-mod_nonspatial_log %>%
spread_draws(b_TyphoonPost, `b_TyphoonPost:Species_IDOtus_elegans`, r_Site_ID[Site_ID,]) %>%
mutate(site_mean = b_TyphoonPost + `b_TyphoonPost:Species_IDOtus_elegans` + r_Site_ID) # get posterior distribution data for plotting
mut_mod$sig<-0
for (i in 1:length(unique(mut_mod$Site_ID))) {
temp<-mut_mod$site_mean[mut_mod$Site_ID %in% mut_mod$Site_ID[i]] %>%
quantile(c(0.05,0.95)) %>% # get 95% credible intervals
sign() %>% # get sign of those intervals
duplicated() # are the signs identical (no = zero-spanning)
if(temp[2] %in% TRUE){ # if signs are identical (non-zero-spanning)
mut_mod$sig[mut_mod$Site_ID %in% mut_mod$Site_ID[i]]<-1 # add significance indicator
}
} # determine whether credible intervals span zero, and store as binary (to show in plot):
rm(temp)
mut_mod$sig<-mut_mod$sig %>% as.character() %>% parse_factor(levels = c('0','1')) # convert to factor
otusplot <-mut_mod %>%
ggplot(aes(y = Site_ID %>% as.character %>% parse_factor(levels = Site_order),
x = site_mean,
fill = stat(x) > 0,
shape = sig)) +
stat_halfeye(show.legend = F,slab_type = "pdf") +
geom_vline(xintercept = 0,
linetype = "dashed") +
scale_fill_manual(values = c("gray80","skyblue")) +
scale_shape_manual(values = c(21,8)) +
labs(x = "Posterior draws (typhoon effect)",
y = "") +
cowplot::theme_minimal_grid()
# plots #
plot_grid(corvusplot, horornisplot, otusplot,
ncol = 3, align = "hv",
labels = c("Corvus", "Horonis", "Otus"))
bird detection temporal variability
Total detections - typhoon effect
dat_pre<-tidy.stability_bird %>%
filter(Cutoff %in% 0.5 & response_variable %in% 'Pre_Var')
dat_post<-tidy.stability_bird %>%
filter(Cutoff %in% 0.5 & response_variable %in% 'Post_Var')
dat_pre<-dat_pre[complete.cases(dat_pre),]
dat_post<-dat_post[complete.cases(dat_post),]
dat_pre$Typhoon = rep("Pre", nrow(dat_pre))
dat_post$Typhoon = rep("Post", nrow(dat_post))
df <- rbind(dat_pre, dat_post)
# change levels for model
df$Typhoon<-df$Typhoon %>% parse_character() %>% parse_factor(levels = c('Pre','Post'))
df$Landuse<-df$Landuse %>% parse_character() %>% parse_factor(levels = c('Forest','Developed'))
df$Stability<-df$Stability+1 # try this to run lognormal model (cannot have zero values)
# set weakly informative priors
all_priors<-c(
set_prior("normal(0, 2)",coef = "LanduseDeveloped"),
set_prior("normal(0, 2)",coef = "LanduseDeveloped:Species_IDHorornis_diphone"),
set_prior("normal(0, 2)",coef = "LanduseDeveloped:Species_IDOtus_elegans"),
set_prior("normal(0, 2)",coef = "LanduseDeveloped:TyphoonPost"),
set_prior("normal(0, 2)",coef = "Species_IDHorornis_diphone"),
set_prior("normal(0, 2)",coef = "Species_IDOtus_elegans"),
set_prior("normal(0, 2)",coef = "TyphoonPost"),
set_prior("normal(0, 2)",coef = "TyphoonPost:Species_IDHorornis_diphone"),
set_prior("normal(0, 2)",coef = "TyphoonPost:Species_IDOtus_elegans"))
# Fit bayesian mixed effects model
mod_nonspatial_log <-
brm(data = df, family = lognormal(),
round(Stability) ~ 1 + Landuse + Typhoon + Species_ID + # used round() as poisson needs integers
Landuse:Typhoon + Landuse:Species_ID + Typhoon:Species_ID + (1|Site_ID),
iter = 5e4, warmup = 5000, chains = 4, cores = 4, thin = 2,
seed = 666, prior = all_priors)## Compiling Stan program...## Trying to compile a simple C file## Running /Library/Frameworks/R.framework/Resources/bin/R CMD SHLIB foo.c
## clang -mmacosx-version-min=10.13 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/Rcpp/include/" -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/" -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/unsupported" -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/BH/include" -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/StanHeaders/include/src/" -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/StanHeaders/include/" -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppParallel/include/" -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/rstan/include" -DEIGEN_NO_DEBUG -DBOOST_DISABLE_ASSERTS -DBOOST_PENDING_INTEGER_LOG2_HPP -DSTAN_THREADS -DBOOST_NO_AUTO_PTR -include '/Library/Frameworks/R.framework/Versions/4.2/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp' -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1 -I/usr/local/include -fPIC -Wall -g -O2 -c foo.c -o foo.o
## In file included from <built-in>:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.2/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
## In file included from /Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/Eigen/Dense:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/Eigen/Core:88:
## /Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:628:1: error: unknown type name 'namespace'
## namespace Eigen {
## ^
## /Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:628:16: error: expected ';' after top level declarator
## namespace Eigen {
## ^
## ;
## In file included from <built-in>:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.2/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
## In file included from /Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/Eigen/Dense:1:
## /Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/Eigen/Core:96:10: fatal error: 'complex' file not found
## #include <complex>
## ^~~~~~~~~
## 3 errors generated.
## make: *** [foo.o] Error 1## Start samplingbrms::mcmc_plot(mod_nonspatial_log, type = "trace") + theme_cowplot()## No divergences to plot.
#check for agreement of chains
brms::mcmc_plot(mod_nonspatial_log, type = "dens_overlay") + theme_cowplot()
# plot posterior probabilities
brms::mcmc_plot(mod_nonspatial_log, type = "intervals",prob = 0.68, prob_outer = 0.95, variable = "^b_", regex = TRUE) + theme_cowplot()
pp_check(mod_nonspatial_log, ndraws = 500) + theme_cowplot() + scale_x_continuous(limits = c(0,50)) # good fit to data
bayes_R2(mod_nonspatial_log)## Estimate Est.Error Q2.5 Q97.5
## R2 0.5499843 0.05630999 0.4216702 0.6392163bres <- residuals(mod_nonspatial_log)[,"Estimate"]
d_mat = as.matrix(dist(df[,c("Lat", "Long")], diag=T, upper=T))
d_mat_inv <- 1/d_mat
d_mat_inv[which(d_mat_inv == Inf)] <- 0
ape::Moran.I(bres, d_mat_inv)## $observed
## [1] 0.007473419
##
## $expected
## [1] -0.01123596
##
## $sd
## [1] 0.02295459
##
## $p.value
## [1] 0.4150378summary(mod_nonspatial_log)## Family: lognormal
## Links: mu = identity; sigma = identity
## Formula: round(Stability) ~ 1 + Landuse + Typhoon + Species_ID + Landuse:Typhoon + Landuse:Species_ID + Typhoon:Species_ID + (1 | Site_ID)
## Data: df (Number of observations: 90)
## Draws: 4 chains, each with iter = 50000; warmup = 5000; thin = 2;
## total post-warmup draws = 90000
##
## Group-Level Effects:
## ~Site_ID (Number of levels: 22)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.11 0.05 0.02 0.22 1.00 34486 38424
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI
## Intercept 0.30 0.08 0.15
## LanduseDeveloped 0.06 0.11 -0.15
## TyphoonPost 0.40 0.09 0.22
## Species_IDHorornis_diphone 0.15 0.10 -0.05
## Species_IDOtus_elegans -0.19 1.42 -2.96
## LanduseDeveloped:TyphoonPost -0.21 0.11 -0.43
## LanduseDeveloped:Species_IDHorornis_diphone 0.08 0.12 -0.15
## LanduseDeveloped:Species_IDOtus_elegans -0.19 1.42 -2.98
## TyphoonPost:Species_IDHorornis_diphone -0.16 0.11 -0.38
## TyphoonPost:Species_IDOtus_elegans -0.09 0.16 -0.40
## u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 0.45 1.00 81187 79497
## LanduseDeveloped 0.27 1.00 80169 79551
## TyphoonPost 0.58 1.00 83773 81496
## Species_IDHorornis_diphone 0.35 1.00 81547 82031
## Species_IDOtus_elegans 2.60 1.00 87734 83733
## LanduseDeveloped:TyphoonPost 0.01 1.00 82614 81457
## LanduseDeveloped:Species_IDHorornis_diphone 0.31 1.00 80730 81054
## LanduseDeveloped:Species_IDOtus_elegans 2.58 1.00 87807 82688
## TyphoonPost:Species_IDHorornis_diphone 0.06 1.00 81362 80980
## TyphoonPost:Species_IDOtus_elegans 0.22 1.00 75306 78622
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 0.24 0.02 0.20 0.29 1.00 54179 68818
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).Plot typhoon effect:
mut_mod<-mod_nonspatial_log %>%
spread_draws(b_TyphoonPost, r_Site_ID[Site_ID,]) %>%
mutate(site_mean = b_TyphoonPost + r_Site_ID) # get posterior distribution data for plotting
mut_mod$sig<-0
for (i in 1:length(unique(mut_mod$Site_ID))) {
temp<-mut_mod$site_mean[mut_mod$Site_ID %in% mut_mod$Site_ID[i]] %>%
quantile(c(0.05,0.95)) %>% # get 95% credible intervals
sign() %>% # get sign of those intervals
duplicated() # are the signs identical (no = zero-spanning)
if(temp[2] %in% TRUE){ # if signs are identical (non-zero-spanning)
mut_mod$sig[mut_mod$Site_ID %in% mut_mod$Site_ID[i]]<-1 # add significance indicator
}
} # determine whether credible intervals span zero, and store as binary (to show in plot):
rm(temp)
mut_mod$sig<-mut_mod$sig %>% as.character() %>% parse_factor(levels = c('0','1')) # convert to factor
mut_mod %>%
ggplot(aes(y = Site_ID %>% as.character %>% parse_factor(levels = Site_order),
x = site_mean,
fill = stat(x) > 0,
shape = sig)) +
stat_halfeye(show.legend = F,slab_type = "pdf") +
geom_vline(xintercept = 0,
linetype = "dashed") +
scale_fill_manual(values = c("gray80","skyblue")) +
scale_shape_manual(values = c(21,8)) +
labs(x = "Posterior draws (typhoon effect)",
y = "") +
cowplot::theme_minimal_grid()
bird detection spatial variability
bird detections - species effect
# prepare data
df<-tidy.spatial_bird %>%
filter(response_group %in% 'Total_Var') # get pre vs post data for comparison
df2<-df
df2$Date<-df2$Date %>% julian.Date() %>% as.numeric() # convert to numeric date format for model
df2$Date<-df2$Date - min(df2$Date) # scale date to start at zero for model
# split out time series for each species class separately
temp<-model.matrix(~ 0 + df2$Species)*df2$Date
date_Corv<-temp[,1]
date_Horo<-temp[,2]
date_Otus<-temp[,3]
rm(temp)
#hist(df$Stability) # looks lognormal
olm<-glm(Stability ~ 0 + Species + date_Corv + date_Horo + date_Otus,
data = df2,family = gaussian(link = 'log')) # linear model
summary(olm)##
## Call:
## glm(formula = Stability ~ 0 + Species + date_Corv + date_Horo +
## date_Otus, family = gaussian(link = "log"), data = df2)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.39145 -0.31137 -0.06864 0.23009 2.86613
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## SpeciesCorvus_macrorhynchos 0.2944319 0.0983584 2.993 0.00312 **
## SpeciesHorornis_diphone 0.2368806 0.0839597 2.821 0.00528 **
## SpeciesOtus_elegans 1.1909561 0.0400509 29.736 < 2e-16 ***
## date_Corv 0.0001205 0.0025663 0.047 0.96260
## date_Horo 0.0121436 0.0018460 6.578 4.36e-10 ***
## date_Otus 0.0006172 0.0010304 0.599 0.54989
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.2997473)
##
## Null deviance: 501.199 on 199 degrees of freedom
## Residual deviance: 57.851 on 193 degrees of freedom
## AIC: 332.89
##
## Number of Fisher Scoring iterations: 5#plot(olm)
# get break points
Psi_1<-df$Date[df$Period %in% 'Trami'][1] %>% julian.Date() %>% as.numeric() - df$Date[1] %>% julian.Date() %>% as.numeric()
Psi_2<-df$Date[df$Period %in% 'Post-typhoon'][1] %>% julian.Date() %>% as.numeric() - df$Date[1] %>% julian.Date() %>% as.numeric()
## break-point model ##
os <- segmented(olm,
seg.Z = ~ date_Corv + date_Horo + date_Otus, # which variables to break
psi = list(date_Corv=c(Psi_1,Psi_2),
date_Horo=c(Psi_1,Psi_2),
date_Otus=c(Psi_1,Psi_2)),
control = seg.control(it.max = 0)) # it.max = 0 prevents bootstrap procedure (i.e. forces manual break-point)
AIC(os,olm) # break point model performs better## df AIC
## os 13 337.9056
## olm 7 332.8869## davies test of the change in slope after break points
davies.test(olm,seg.Z = ~ date_Corv,
values = c(Psi_1,Psi_2)) # test for change in slope of forest sites##
## Davies' test for a change in the slope
##
## data: formula = Stability ~ 0 + Species + date_Corv + date_Horo + date_Otus , method = glm
## model = gaussian , link = log , statist = lrt
## segmented variable = date_Corv
## 'best' at = 30, n.points = 2, p-value = 0.6016
## alternative hypothesis: two.sideddavies.test(olm,seg.Z = ~ date_Horo,
values = c(Psi_1,Psi_2)) # test for change in slope of developed sites##
## Davies' test for a change in the slope
##
## data: formula = Stability ~ 0 + Species + date_Corv + date_Horo + date_Otus , method = glm
## model = gaussian , link = log , statist = lrt
## segmented variable = date_Horo
## 'best' at = 30, n.points = 2, p-value = 0.3709
## alternative hypothesis: two.sideddavies.test(olm,seg.Z = ~ date_Otus,
values = c(Psi_1,Psi_2)) # test for change in slope of developed sites##
## Davies' test for a change in the slope
##
## data: formula = Stability ~ 0 + Species + date_Corv + date_Horo + date_Otus , method = glm
## model = gaussian , link = log , statist = lrt
## segmented variable = date_Otus
## 'best' at = 37, n.points = 2, p-value = 0.6549
## alternative hypothesis: two.sidedsummary(os)##
## Call:
## glm(formula = Stability ~ Species + date_Corv + date_Horo + date_Otus +
## U1.date_Corv + U2.date_Corv + U1.date_Horo + U2.date_Horo +
## U1.date_Otus + U2.date_Otus - 1, family = gaussian(link = "log"),
## data = mfExt)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.32267 -0.36387 -0.03511 0.20805 2.44537
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## SpeciesCorvus_macrorhynchos 0.3953437 0.1334299 2.963 0.003443 **
## SpeciesHorornis_diphone 0.0144594 0.1491537 0.097 0.922876
## SpeciesOtus_elegans 1.1856549 0.0582577 20.352 < 2e-16 ***
## date_Corv -0.0077334 0.0077263 -1.001 0.318162
## date_Horo 0.0264756 0.0068424 3.869 0.000151 ***
## date_Otus 0.0006354 0.0033142 0.192 0.848179
## U1.date_Corv 0.0337655 0.0340437 0.992 0.322563
## U2.date_Corv -0.0275707 0.0336930 -0.818 0.414231
## U1.date_Horo -0.0552909 0.0235015 -2.353 0.019679 *
## U2.date_Horo 0.0444937 0.0222013 2.004 0.046502 *
## U1.date_Otus 0.0047852 0.0140683 0.340 0.734133
## U2.date_Otus -0.0066243 0.0137481 -0.482 0.630486
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.2987036)
##
## Null deviance: 501.199 on 199 degrees of freedom
## Residual deviance: 55.857 on 187 degrees of freedom
## AIC: 337.91
##
## Number of Fisher Scoring iterations: 6# compare pre- and post-break acoustic index values
Break_results<-df %>%
filter(Period %in% c('Pre-typhoon','Post-typhoon')) %>%
group_by(Species,Period) %>%
summarise(Med=median(Stability),
Lo_95=quantile(Stability,probs = 0.05),
Up_95=quantile(Stability,probs = 0.95))## `summarise()` has grouped output by 'Species'. You can override using the
## `.groups` argument.Break_results$Period<-Break_results$Period %>% as.character() %>% parse_factor(levels = c('Pre-typhoon','Post-typhoon'))
Break_results$Species<-Break_results$Species %>% as.character() %>% parse_factor(levels = c('Corvus_macrorhynchos','Horornis_diphone','Otus_elegans'))Plot results:
Sp_labs<-c('C. macrorhynchos','H. diphone','O. elegans')
names(Sp_labs)<-c('Corvus_macrorhynchos','Horornis_diphone','Otus_elegans')
plot1<-df %>% ggplot(aes(x = Date,
y = Stability,
alpha = Species)) +
scale_alpha_manual(values = c(1,0.6,0.3)) +
# scale_colour_manual(values = c("black","grey50","grey80")) +
geom_vline(xintercept = df$Date[df$Period %in% 'Trami'][1],lty = 2) +
geom_vline(xintercept = df$Date[df$Period %in% 'Post-typhoon'][1],lty = 2) +
geom_line(show.legend = F) +
labs(x = "Time",
y = "Species detection spatial variability") +
theme_cowplot(12) +
ylim(c(df$Stability %>% min(),
df$Stability %>% max()))
plot2<-Break_results %>% ggplot(aes(x = Species,
y = Med,
ymin = Lo_95,
ymax = Up_95,
alpha = Species,
shape = Period)) +
scale_alpha_manual(values = c(1,0.6,0.3)) +
# scale_colour_manual(values = c("#29a62b","#a629a4")) +
geom_pointrange(position = position_jitterdodge(),
fatten = 5,
show.legend = F) +
labs(x = "Species",
y = element_blank()) +
scale_x_discrete(labels = Sp_labs) +
theme_cowplot(12) +
ylim(c(df$Stability %>% min(),
df$Stability %>% max()))
panel_a<-plot1 + plot2 + plot_layout(widths = c(3,1))
panel_a
Now check for landuse effect:
# prepare data
df<-tidy.spatial_bird %>%
filter(response_group %in% c('Forest_Var','Developed_Var')) # get pre vs post data for comparison
df$response_group[df$response_group %in% 'Forest_Var']<-'Forest'
df$response_group[df$response_group %in% 'Developed_Var']<-'Developed'
colnames(df)[4]<-'Landuse'
df$Landuse<-df$Landuse %>% as.character() %>% parse_factor(levels = c('Forest','Developed')) # change levels for model
df$Stability[df$Species %in% 'Otus_elegans' & df$Landuse %in% 'Forest']<-df$Stability[df$Species %in% 'Otus_elegans' & df$Landuse %in% 'Developed'] # fix error in forest owl data (showing up as developed for some reason)
df$Stability[df$Species %in% 'Otus_elegans' & df$Landuse %in% 'Developed']<-NA # fix error in forest owl data
df2<-df
df2$Date<-df2$Date %>% julian.Date() %>% as.numeric() # convert to numeric date format for model
df2$Date<-df2$Date - min(df2$Date) # scale date to start at zero for model
# split out time series for each species class separately
temp<-model.matrix(~ 0 + df2$Species * df2$Landuse)*df2$Date
date_Corv_for<-temp[,1]
date_Horo_for<-temp[,2]
date_Otus_for<-temp[,3]
date_Corv_dev<-temp[,4]
date_Horo_dev<-temp[,5]
rm(temp)
#hist(df$Stability) # looks lognormal
olm<-glm(Stability ~ 0 + Species + Landuse + date_Corv_for + date_Horo_for + date_Otus_for + date_Corv_dev + date_Horo_dev,
data = df2,family = gaussian(link = 'log')) # linear model
summary(olm)##
## Call:
## glm(formula = Stability ~ 0 + Species + Landuse + date_Corv_for +
## date_Horo_for + date_Otus_for + date_Corv_dev + date_Horo_dev,
## family = gaussian(link = "log"), data = df2)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.06328 -0.29742 -0.06402 0.21754 1.56347
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## SpeciesCorvus_macrorhynchos 0.4530744 0.0661333 6.851 3.77e-11 ***
## SpeciesHorornis_diphone 0.4034000 0.0583292 6.916 2.53e-11 ***
## SpeciesOtus_elegans 0.7621943 0.0504132 15.119 < 2e-16 ***
## LanduseDeveloped -0.3637955 0.0785195 -4.633 5.25e-06 ***
## date_Corv_for -0.0055654 0.0019943 -2.791 0.005576 **
## date_Horo_for 0.0079596 0.0013982 5.693 2.83e-08 ***
## date_Otus_for 0.0006876 0.0012955 0.531 0.595955
## date_Corv_dev 0.0111716 0.0023546 4.745 3.15e-06 ***
## date_Horo_dev -0.0065366 0.0018524 -3.529 0.000479 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.2019162)
##
## Null deviance: 275.501 on 329 degrees of freedom
## Residual deviance: 64.613 on 320 degrees of freedom
## (69 observations deleted due to missingness)
## AIC: 418.17
##
## Number of Fisher Scoring iterations: 5#plot(olm)
# get break points
Psi_1<-df$Date[df$Period %in% 'Trami'][1] %>% julian.Date() %>% as.numeric() - df$Date[1] %>% julian.Date() %>% as.numeric()
Psi_2<-df$Date[df$Period %in% 'Post-typhoon'][1] %>% julian.Date() %>% as.numeric() - df$Date[1] %>% julian.Date() %>% as.numeric()
## break-point model ##
os <- segmented(olm,
seg.Z = ~ date_Corv_for + date_Horo_for + date_Otus_for + date_Corv_dev + date_Horo_dev, # which variables to break
psi = list(date_Corv_for=c(Psi_1,Psi_2),
date_Horo_for=c(Psi_1,Psi_2),
date_Otus_for=c(Psi_1,Psi_2),
date_Corv_dev=c(Psi_1,Psi_2),
date_Horo_dev=c(Psi_1,Psi_2)),
control = seg.control(it.max = 0)) # it.max = 0 prevents bootstrap procedure (i.e. forces manual break-point)
AIC(os,olm) # break point model performs better## df AIC
## os 20 421.9435
## olm 10 418.1682## davies test of the change in slope after break points
davies.test(olm,seg.Z = ~ date_Corv_for,
values = c(Psi_1,Psi_2)) # test for change in slope of forest sites##
## Davies' test for a change in the slope
##
## data: formula = Stability ~ 0 + Species + Landuse + date_Corv_for + date_Horo_for + date_Otus_for + date_Corv_dev + date_Horo_dev , method = glm
## model = gaussian , link = log , statist = lrt
## segmented variable = date_Corv_for
## 'best' at = 30, n.points = 2, p-value = 0.7488
## alternative hypothesis: two.sideddavies.test(olm,seg.Z = ~ date_Horo_for,
values = c(Psi_1,Psi_2)) # test for change in slope of developed sites##
## Davies' test for a change in the slope
##
## data: formula = Stability ~ 0 + Species + Landuse + date_Corv_for + date_Horo_for + date_Otus_for + date_Corv_dev + date_Horo_dev , method = glm
## model = gaussian , link = log , statist = lrt
## segmented variable = date_Horo_for
## 'best' at = 30, n.points = 2, p-value = 0.2722
## alternative hypothesis: two.sideddavies.test(olm,seg.Z = ~ date_Otus_for,
values = c(Psi_1,Psi_2)) # test for change in slope of developed sites##
## Davies' test for a change in the slope
##
## data: formula = Stability ~ 0 + Species + Landuse + date_Corv_for + date_Horo_for + date_Otus_for + date_Corv_dev + date_Horo_dev , method = glm
## model = gaussian , link = log , statist = lrt
## segmented variable = date_Otus_for
## 'best' at = 37, n.points = 2, p-value = 0.6868
## alternative hypothesis: two.sideddavies.test(olm,seg.Z = ~ date_Corv_dev,
values = c(Psi_1,Psi_2)) # test for change in slope of forest sites##
## Davies' test for a change in the slope
##
## data: formula = Stability ~ 0 + Species + Landuse + date_Corv_for + date_Horo_for + date_Otus_for + date_Corv_dev + date_Horo_dev , method = glm
## model = gaussian , link = log , statist = lrt
## segmented variable = date_Corv_dev
## 'best' at = 37, n.points = 2, p-value = 0.2356
## alternative hypothesis: two.sideddavies.test(olm,seg.Z = ~ date_Horo_dev,
values = c(Psi_1,Psi_2)) # test for change in slope of developed sites##
## Davies' test for a change in the slope
##
## data: formula = Stability ~ 0 + Species + Landuse + date_Corv_for + date_Horo_for + date_Otus_for + date_Corv_dev + date_Horo_dev , method = glm
## model = gaussian , link = log , statist = lrt
## segmented variable = date_Horo_dev
## 'best' at = 37, n.points = 2, p-value = 0.3547
## alternative hypothesis: two.sidedsummary(os)##
## Call:
## glm(formula = Stability ~ Species + Landuse + date_Corv_for +
## date_Horo_for + date_Otus_for + date_Corv_dev + date_Horo_dev +
## U1.date_Corv_for + U2.date_Corv_for + U1.date_Horo_for +
## U2.date_Horo_for + U1.date_Otus_for + U2.date_Otus_for +
## U1.date_Corv_dev + U2.date_Corv_dev + U1.date_Horo_dev +
## U2.date_Horo_dev - 1, family = gaussian(link = "log"), data = mfExt)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.01115 -0.29632 -0.04608 0.20484 1.58962
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## SpeciesCorvus_macrorhynchos 0.5281456 0.0891979 5.921 8.48e-09 ***
## SpeciesHorornis_diphone 0.2819640 0.0913602 3.086 0.002210 **
## SpeciesOtus_elegans 0.7572079 0.0728795 10.390 < 2e-16 ***
## LanduseDeveloped -0.3012331 0.1178538 -2.556 0.011065 *
## date_Corv_for -0.0118111 0.0055460 -2.130 0.033988 *
## date_Horo_for 0.0171096 0.0045190 3.786 0.000184 ***
## date_Otus_for 0.0006118 0.0041458 0.148 0.882769
## date_Corv_dev 0.0053295 0.0076618 0.696 0.487206
## date_Horo_dev -0.0082786 0.0063805 -1.297 0.195434
## U1.date_Corv_for 0.0241600 0.0275393 0.877 0.381008
## U2.date_Corv_for -0.0170674 0.0287538 -0.594 0.553232
## U1.date_Horo_for -0.0449296 0.0182538 -2.461 0.014385 *
## U2.date_Horo_for 0.0406309 0.0182906 2.221 0.027045 *
## U1.date_Otus_for 0.0059812 0.0175685 0.340 0.733750
## U2.date_Otus_for -0.0080399 0.0171463 -0.469 0.639472
## U1.date_Corv_dev 0.0472932 0.0384173 1.231 0.219242
## U2.date_Corv_dev -0.0525663 0.0390245 -1.347 0.178961
## U1.date_Horo_dev 0.0124560 0.0446125 0.279 0.780274
## U2.date_Horo_dev -0.0102703 0.0471741 -0.218 0.827797
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1984008)
##
## Null deviance: 275.501 on 329 degrees of freedom
## Residual deviance: 61.504 on 310 degrees of freedom
## AIC: 421.94
##
## Number of Fisher Scoring iterations: 6# compare pre- and post-break acoustic index values
Break_results<-df %>%
filter(Period %in% c('Pre-typhoon','Post-typhoon')) %>%
group_by(Species,Landuse,Period) %>%
summarise(Med=median(Stability,na.rm = T),
Lo_95=quantile(Stability,probs = 0.05,na.rm = T),
Up_95=quantile(Stability,probs = 0.95,na.rm = T))## `summarise()` has grouped output by 'Species', 'Landuse'. You can override
## using the `.groups` argument.Break_results$Period<-Break_results$Period %>% as.character() %>% parse_factor(levels = c('Pre-typhoon','Post-typhoon'))
Break_results$Landuse<-Break_results$Landuse %>% as.character() %>% parse_factor(levels = c('Forest','Developed'))
Break_results$Species<-Break_results$Species %>% as.character() %>% parse_factor(levels = c('Corvus_macrorhynchos','Horornis_diphone','Otus_elegans'))Plot results:
plot1<-df %>% ggplot(aes(x = Date,
y = Stability,
alpha = Species,
col = Landuse)) +
scale_colour_manual(values = c("#29a62b","#a629a4")) +
scale_alpha_manual(values = c(1,0.6,0.3)) +
geom_vline(xintercept = df$Date[df$Period %in% 'Trami'][1],lty = 2) +
geom_vline(xintercept = df$Date[df$Period %in% 'Post-typhoon'][1],lty = 2) +
geom_line(show.legend = F) +
labs(x = "Time",
y = "Species detection spatial variability") +
theme_cowplot(12) +
theme(strip.background = element_rect(fill = 'white')) +
ylim(c(df$Stability %>% min(),
df$Stability %>% max())) +
facet_grid(Species ~ .,
labeller = labeller(Species = Sp_labs))
plot2<-Break_results %>% ggplot(aes(x = Species,
y = Med,
ymin = Lo_95,
ymax = Up_95,
alpha = Species,
col = Landuse,
shape = Period)) +
scale_alpha_manual(values = c(1,0.6,0.3)) +
scale_colour_manual(values = c("#29a62b","#a629a4")) +
geom_pointrange(position = position_jitterdodge(),
fatten = 5,
show.legend = F) +
labs(x = 'Species',
y = element_blank()) +
scale_x_discrete(labels = Sp_labs) +
theme_cowplot(12) +
ylim(c(df$Stability %>% min(),
df$Stability %>% max()))
panel_b<-plot1 + plot2 + plot_layout(widths = c(3,1))
panel_b## Warning: Removed 65 row(s) containing missing values (geom_path).## Warning: Removed 2 rows containing missing values (geom_pointrange).
panel_a / panel_b## Warning: Removed 65 row(s) containing missing values (geom_path).## Warning: Removed 2 rows containing missing values (geom_pointrange).
** Do SAME BUT FOR DIFFERENT TIME HORIZONS BEFORE AND AFTER TYPHOONS - TO SHOW RESULTS DON’T DEPEND ON TIME BEFORE/AFTER TYPHOONS
Figure S2
Finally, compare different automatic filtering thresholds for species detection confidence. Test the mean species effect on Horonis_diphone under three confidence thresholds. NB: to speed this up, we’ll run 10x fewer iterations in the models.
Threshold = 0.5
dat_pre<-tidy.stability_bird %>%
filter(Cutoff %in% 0.5 & response_variable %in% 'Pre_mean')
dat_post<-tidy.stability_bird %>%
filter(Cutoff %in% 0.5 & response_variable %in% 'Post_mean')
dat_pre<-dat_pre[complete.cases(dat_pre),]
dat_post<-dat_post[complete.cases(dat_post),]
dat_pre$Typhoon = rep("Pre", nrow(dat_pre))
dat_post$Typhoon = rep("Post", nrow(dat_post))
df <- rbind(dat_pre, dat_post)
# change levels for model
df$Typhoon<-df$Typhoon %>% parse_character() %>% parse_factor(levels = c('Pre','Post'))
df$Landuse<-df$Landuse %>% parse_character() %>% parse_factor(levels = c('Forest','Developed'))
df$Stability<-df$Stability+1 # try this to run lognormal model (cannot have zero values)
# set weakly informative priors
all_priors<-c(
set_prior("normal(0, 2)",coef = "LanduseDeveloped"),
set_prior("normal(0, 2)",coef = "LanduseDeveloped:Species_IDHorornis_diphone"),
set_prior("normal(0, 2)",coef = "LanduseDeveloped:Species_IDOtus_elegans"),
set_prior("normal(0, 2)",coef = "LanduseDeveloped:TyphoonPost"),
set_prior("normal(0, 2)",coef = "Species_IDHorornis_diphone"),
set_prior("normal(0, 2)",coef = "Species_IDOtus_elegans"),
set_prior("normal(0, 2)",coef = "TyphoonPost"),
set_prior("normal(0, 2)",coef = "TyphoonPost:Species_IDHorornis_diphone"),
set_prior("normal(0, 2)",coef = "TyphoonPost:Species_IDOtus_elegans"))
# Fit bayesian mixed effects model
mod_nonspatial_log <-
brm(data = df, family = lognormal(),
round(Stability) ~ 1 + Landuse + Typhoon + Species_ID + # used round() as poisson needs integers
Landuse:Typhoon + Landuse:Species_ID + Typhoon:Species_ID + (1|Site_ID),
iter = 5e3, warmup = 500, chains = 4, cores = 4, thin = 2,
seed = 666, prior = all_priors)## Compiling Stan program...## Start samplingsummary(mod_nonspatial_log)## Family: lognormal
## Links: mu = identity; sigma = identity
## Formula: round(Stability) ~ 1 + Landuse + Typhoon + Species_ID + Landuse:Typhoon + Landuse:Species_ID + Typhoon:Species_ID + (1 | Site_ID)
## Data: df (Number of observations: 90)
## Draws: 4 chains, each with iter = 5000; warmup = 500; thin = 2;
## total post-warmup draws = 9000
##
## Group-Level Effects:
## ~Site_ID (Number of levels: 22)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.69 0.18 0.37 1.08 1.00 4387 6505
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI
## Intercept 3.58 0.32 2.94
## LanduseDeveloped 0.26 0.44 -0.60
## TyphoonPost -0.18 0.32 -0.80
## Species_IDHorornis_diphone -1.06 0.35 -1.76
## Species_IDOtus_elegans -0.46 1.43 -3.26
## LanduseDeveloped:TyphoonPost -0.41 0.38 -1.16
## LanduseDeveloped:Species_IDHorornis_diphone -0.33 0.41 -1.12
## LanduseDeveloped:Species_IDOtus_elegans -0.49 1.43 -3.28
## TyphoonPost:Species_IDHorornis_diphone -1.25 0.39 -2.01
## TyphoonPost:Species_IDOtus_elegans -0.11 0.55 -1.17
## u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 4.19 1.00 6665 7545
## LanduseDeveloped 1.11 1.00 6387 7500
## TyphoonPost 0.45 1.00 8400 8296
## Species_IDHorornis_diphone -0.37 1.00 7949 7780
## Species_IDOtus_elegans 2.34 1.00 8218 8330
## LanduseDeveloped:TyphoonPost 0.35 1.00 8465 8083
## LanduseDeveloped:Species_IDHorornis_diphone 0.49 1.00 7827 8121
## LanduseDeveloped:Species_IDOtus_elegans 2.24 1.00 8215 8226
## TyphoonPost:Species_IDHorornis_diphone -0.49 1.00 8137 7959
## TyphoonPost:Species_IDOtus_elegans 0.94 1.00 7646 8260
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 0.86 0.08 0.72 1.04 1.00 6321 7697
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).#Plot - Horornis #
mut_mod<-mod_nonspatial_log %>%
spread_draws(b_Species_IDHorornis_diphone, r_Site_ID[Site_ID,]) %>%
mutate(site_mean = b_Species_IDHorornis_diphone + r_Site_ID) # get posterior distribution data for plotting
mut_mod$sig<-0
for (i in 1:length(unique(mut_mod$Site_ID))) {
temp<-mut_mod$site_mean[mut_mod$Site_ID %in% mut_mod$Site_ID[i]] %>%
quantile(c(0.05,0.95)) %>% # get 95% credible intervals
sign() %>% # get sign of those intervals
duplicated() # are the signs identical (no = zero-spanning)
if(temp[2] %in% TRUE){ # if signs are identical (non-zero-spanning)
mut_mod$sig[mut_mod$Site_ID %in% mut_mod$Site_ID[i]]<-1 # add significance indicator
}
} # determine whether credible intervals span zero, and store as binary (to show in plot):
rm(temp)
mut_mod$sig<-mut_mod$sig %>% as.character() %>% parse_factor(levels = c('0','1')) # convert to factor
Plot_50 <- mut_mod %>%
ggplot(aes(y = Site_ID %>% as.character %>% parse_factor(levels = Site_order),
x = site_mean,
fill = stat(x) > 0,
shape = sig)) +
stat_halfeye(show.legend = F,slab_type = "pdf") +
geom_vline(xintercept = 0,
linetype = "dashed") +
scale_fill_manual(values = c("gray80","navajowhite")) +
scale_shape_manual(values = c(21,8)) +
labs(x = "Posterior draws (species effect)",
y = "") +
cowplot::theme_minimal_grid()Threshold = 0.75
dat_pre<-tidy.stability_bird %>%
filter(Cutoff %in% 0.75 & response_variable %in% 'Pre_mean')
dat_post<-tidy.stability_bird %>%
filter(Cutoff %in% 0.75 & response_variable %in% 'Post_mean')
dat_pre<-dat_pre[complete.cases(dat_pre),]
dat_post<-dat_post[complete.cases(dat_post),]
dat_pre$Typhoon = rep("Pre", nrow(dat_pre))
dat_post$Typhoon = rep("Post", nrow(dat_post))
df <- rbind(dat_pre, dat_post)
# change levels for model
df$Typhoon<-df$Typhoon %>% parse_character() %>% parse_factor(levels = c('Pre','Post'))
df$Landuse<-df$Landuse %>% parse_character() %>% parse_factor(levels = c('Forest','Developed'))
df$Stability<-df$Stability+1 # try this to run lognormal model (cannot have zero values)
# set weakly informative priors
all_priors<-c(
set_prior("normal(0, 2)",coef = "LanduseDeveloped"),
set_prior("normal(0, 2)",coef = "LanduseDeveloped:Species_IDHorornis_diphone"),
set_prior("normal(0, 2)",coef = "LanduseDeveloped:Species_IDOtus_elegans"),
set_prior("normal(0, 2)",coef = "LanduseDeveloped:TyphoonPost"),
set_prior("normal(0, 2)",coef = "Species_IDHorornis_diphone"),
set_prior("normal(0, 2)",coef = "Species_IDOtus_elegans"),
set_prior("normal(0, 2)",coef = "TyphoonPost"),
set_prior("normal(0, 2)",coef = "TyphoonPost:Species_IDHorornis_diphone"),
set_prior("normal(0, 2)",coef = "TyphoonPost:Species_IDOtus_elegans"))
# Fit bayesian mixed effects model
mod_nonspatial_log <-
brm(data = df, family = lognormal(),
round(Stability) ~ 1 + Landuse + Typhoon + Species_ID + # used round() as poisson needs integers
Landuse:Typhoon + Landuse:Species_ID + Typhoon:Species_ID + (1|Site_ID),
iter = 5e3, warmup = 500, chains = 4, cores = 4, thin = 2,
seed = 666, prior = all_priors)## Compiling Stan program...## Trying to compile a simple C file## Running /Library/Frameworks/R.framework/Resources/bin/R CMD SHLIB foo.c
## clang -mmacosx-version-min=10.13 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/Rcpp/include/" -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/" -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/unsupported" -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/BH/include" -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/StanHeaders/include/src/" -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/StanHeaders/include/" -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppParallel/include/" -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/rstan/include" -DEIGEN_NO_DEBUG -DBOOST_DISABLE_ASSERTS -DBOOST_PENDING_INTEGER_LOG2_HPP -DSTAN_THREADS -DBOOST_NO_AUTO_PTR -include '/Library/Frameworks/R.framework/Versions/4.2/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp' -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1 -I/usr/local/include -fPIC -Wall -g -O2 -c foo.c -o foo.o
## In file included from <built-in>:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.2/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
## In file included from /Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/Eigen/Dense:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/Eigen/Core:88:
## /Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:628:1: error: unknown type name 'namespace'
## namespace Eigen {
## ^
## /Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:628:16: error: expected ';' after top level declarator
## namespace Eigen {
## ^
## ;
## In file included from <built-in>:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.2/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
## In file included from /Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/Eigen/Dense:1:
## /Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/Eigen/Core:96:10: fatal error: 'complex' file not found
## #include <complex>
## ^~~~~~~~~
## 3 errors generated.
## make: *** [foo.o] Error 1## Start samplingsummary(mod_nonspatial_log)## Family: lognormal
## Links: mu = identity; sigma = identity
## Formula: round(Stability) ~ 1 + Landuse + Typhoon + Species_ID + Landuse:Typhoon + Landuse:Species_ID + Typhoon:Species_ID + (1 | Site_ID)
## Data: df (Number of observations: 90)
## Draws: 4 chains, each with iter = 5000; warmup = 500; thin = 2;
## total post-warmup draws = 9000
##
## Group-Level Effects:
## ~Site_ID (Number of levels: 22)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.78 0.17 0.50 1.16 1.00 4324 5764
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI
## Intercept 4.90 0.30 4.30
## LanduseDeveloped 0.27 0.42 -0.56
## TyphoonPost -0.18 0.25 -0.67
## Species_IDHorornis_diphone -1.16 0.29 -1.72
## Species_IDOtus_elegans -0.79 1.43 -3.63
## LanduseDeveloped:TyphoonPost -0.62 0.31 -1.23
## LanduseDeveloped:Species_IDHorornis_diphone -0.41 0.33 -1.06
## LanduseDeveloped:Species_IDOtus_elegans -0.82 1.43 -3.59
## TyphoonPost:Species_IDHorornis_diphone -0.89 0.31 -1.49
## TyphoonPost:Species_IDOtus_elegans 0.24 0.44 -0.63
## u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 5.47 1.00 5984 7349
## LanduseDeveloped 1.10 1.00 5313 6628
## TyphoonPost 0.33 1.00 8527 8219
## Species_IDHorornis_diphone -0.60 1.00 7613 8018
## Species_IDOtus_elegans 1.98 1.00 7648 7714
## LanduseDeveloped:TyphoonPost -0.02 1.00 8397 8221
## LanduseDeveloped:Species_IDHorornis_diphone 0.24 1.00 8046 7814
## LanduseDeveloped:Species_IDOtus_elegans 2.04 1.00 7398 7625
## TyphoonPost:Species_IDHorornis_diphone -0.28 1.00 8394 8137
## TyphoonPost:Species_IDOtus_elegans 1.09 1.00 8235 8093
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 0.68 0.06 0.57 0.81 1.00 6543 7496
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).#Plot - Horornis #
mut_mod<-mod_nonspatial_log %>%
spread_draws(b_Species_IDHorornis_diphone, r_Site_ID[Site_ID,]) %>%
mutate(site_mean = b_Species_IDHorornis_diphone + r_Site_ID) # get posterior distribution data for plotting
mut_mod$sig<-0
for (i in 1:length(unique(mut_mod$Site_ID))) {
temp<-mut_mod$site_mean[mut_mod$Site_ID %in% mut_mod$Site_ID[i]] %>%
quantile(c(0.05,0.95)) %>% # get 95% credible intervals
sign() %>% # get sign of those intervals
duplicated() # are the signs identical (no = zero-spanning)
if(temp[2] %in% TRUE){ # if signs are identical (non-zero-spanning)
mut_mod$sig[mut_mod$Site_ID %in% mut_mod$Site_ID[i]]<-1 # add significance indicator
}
} # determine whether credible intervals span zero, and store as binary (to show in plot):
rm(temp)
mut_mod$sig<-mut_mod$sig %>% as.character() %>% parse_factor(levels = c('0','1')) # convert to factor
Plot_75 <- mut_mod %>%
ggplot(aes(y = Site_ID %>% as.character %>% parse_factor(levels = Site_order),
x = site_mean,
fill = stat(x) > 0,
shape = sig)) +
stat_halfeye(show.legend = F,slab_type = "pdf") +
geom_vline(xintercept = 0,
linetype = "dashed") +
scale_fill_manual(values = c("gray80","navajowhite")) +
scale_shape_manual(values = c(21,8)) +
labs(x = "Posterior draws (species effect)",
y = "") +
cowplot::theme_minimal_grid()Threshold = 0.9
dat_pre<-tidy.stability_bird %>%
filter(Cutoff %in% 0.9 & response_variable %in% 'Pre_mean')
dat_post<-tidy.stability_bird %>%
filter(Cutoff %in% 0.9 & response_variable %in% 'Post_mean')
dat_pre<-dat_pre[complete.cases(dat_pre),]
dat_post<-dat_post[complete.cases(dat_post),]
dat_pre$Typhoon = rep("Pre", nrow(dat_pre))
dat_post$Typhoon = rep("Post", nrow(dat_post))
df <- rbind(dat_pre, dat_post)
# change levels for model
df$Typhoon<-df$Typhoon %>% parse_character() %>% parse_factor(levels = c('Pre','Post'))
df$Landuse<-df$Landuse %>% parse_character() %>% parse_factor(levels = c('Forest','Developed'))
df$Stability<-df$Stability+1 # try this to run lognormal model (cannot have zero values)
# set weakly informative priors
all_priors<-c(
set_prior("normal(0, 2)",coef = "LanduseDeveloped"),
set_prior("normal(0, 2)",coef = "LanduseDeveloped:Species_IDHorornis_diphone"),
set_prior("normal(0, 2)",coef = "LanduseDeveloped:Species_IDOtus_elegans"),
set_prior("normal(0, 2)",coef = "LanduseDeveloped:TyphoonPost"),
set_prior("normal(0, 2)",coef = "Species_IDHorornis_diphone"),
set_prior("normal(0, 2)",coef = "Species_IDOtus_elegans"),
set_prior("normal(0, 2)",coef = "TyphoonPost"),
set_prior("normal(0, 2)",coef = "TyphoonPost:Species_IDHorornis_diphone"),
set_prior("normal(0, 2)",coef = "TyphoonPost:Species_IDOtus_elegans"))
# Fit bayesian mixed effects model
mod_nonspatial_log <-
brm(data = df, family = lognormal(),
round(Stability) ~ 1 + Landuse + Typhoon + Species_ID + # used round() as poisson needs integers
Landuse:Typhoon + Landuse:Species_ID + Typhoon:Species_ID + (1|Site_ID),
iter = 5e3, warmup = 500, chains = 4, cores = 4, thin = 2,
seed = 666, prior = all_priors)## Compiling Stan program...## Trying to compile a simple C file## Running /Library/Frameworks/R.framework/Resources/bin/R CMD SHLIB foo.c
## clang -mmacosx-version-min=10.13 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/Rcpp/include/" -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/" -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/unsupported" -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/BH/include" -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/StanHeaders/include/src/" -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/StanHeaders/include/" -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppParallel/include/" -I"/Library/Frameworks/R.framework/Versions/4.2/Resources/library/rstan/include" -DEIGEN_NO_DEBUG -DBOOST_DISABLE_ASSERTS -DBOOST_PENDING_INTEGER_LOG2_HPP -DSTAN_THREADS -DBOOST_NO_AUTO_PTR -include '/Library/Frameworks/R.framework/Versions/4.2/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp' -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1 -I/usr/local/include -fPIC -Wall -g -O2 -c foo.c -o foo.o
## In file included from <built-in>:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.2/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
## In file included from /Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/Eigen/Dense:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/Eigen/Core:88:
## /Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:628:1: error: unknown type name 'namespace'
## namespace Eigen {
## ^
## /Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:628:16: error: expected ';' after top level declarator
## namespace Eigen {
## ^
## ;
## In file included from <built-in>:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.2/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
## In file included from /Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/Eigen/Dense:1:
## /Library/Frameworks/R.framework/Versions/4.2/Resources/library/RcppEigen/include/Eigen/Core:96:10: fatal error: 'complex' file not found
## #include <complex>
## ^~~~~~~~~
## 3 errors generated.
## make: *** [foo.o] Error 1## Start samplingsummary(mod_nonspatial_log)## Family: lognormal
## Links: mu = identity; sigma = identity
## Formula: round(Stability) ~ 1 + Landuse + Typhoon + Species_ID + Landuse:Typhoon + Landuse:Species_ID + Typhoon:Species_ID + (1 | Site_ID)
## Data: df (Number of observations: 90)
## Draws: 4 chains, each with iter = 5000; warmup = 500; thin = 2;
## total post-warmup draws = 9000
##
## Group-Level Effects:
## ~Site_ID (Number of levels: 22)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.80 0.17 0.51 1.19 1.00 3990 5853
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI
## Intercept 5.44 0.30 4.85
## LanduseDeveloped 0.30 0.42 -0.52
## TyphoonPost -0.18 0.24 -0.66
## Species_IDHorornis_diphone -1.02 0.27 -1.56
## Species_IDOtus_elegans -0.93 1.41 -3.69
## LanduseDeveloped:TyphoonPost -0.76 0.30 -1.34
## LanduseDeveloped:Species_IDHorornis_diphone -0.42 0.32 -1.05
## LanduseDeveloped:Species_IDOtus_elegans -0.93 1.41 -3.72
## TyphoonPost:Species_IDHorornis_diphone -0.73 0.29 -1.31
## TyphoonPost:Species_IDOtus_elegans 0.43 0.42 -0.42
## u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 6.03 1.00 5267 6329
## LanduseDeveloped 1.15 1.00 4802 5669
## TyphoonPost 0.29 1.00 7431 7359
## Species_IDHorornis_diphone -0.47 1.00 7323 7546
## Species_IDOtus_elegans 1.85 1.00 7933 7593
## LanduseDeveloped:TyphoonPost -0.17 1.00 8144 7776
## LanduseDeveloped:Species_IDHorornis_diphone 0.22 1.00 8146 8082
## LanduseDeveloped:Species_IDOtus_elegans 1.86 1.00 7993 7655
## TyphoonPost:Species_IDHorornis_diphone -0.16 1.00 7091 7662
## TyphoonPost:Species_IDOtus_elegans 1.26 1.00 7407 7348
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 0.65 0.06 0.55 0.79 1.00 6292 7819
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).#Plot - Horornis #
mut_mod<-mod_nonspatial_log %>%
spread_draws(b_Species_IDHorornis_diphone, r_Site_ID[Site_ID,]) %>%
mutate(site_mean = b_Species_IDHorornis_diphone + r_Site_ID) # get posterior distribution data for plotting
mut_mod$sig<-0
for (i in 1:length(unique(mut_mod$Site_ID))) {
temp<-mut_mod$site_mean[mut_mod$Site_ID %in% mut_mod$Site_ID[i]] %>%
quantile(c(0.05,0.95)) %>% # get 95% credible intervals
sign() %>% # get sign of those intervals
duplicated() # are the signs identical (no = zero-spanning)
if(temp[2] %in% TRUE){ # if signs are identical (non-zero-spanning)
mut_mod$sig[mut_mod$Site_ID %in% mut_mod$Site_ID[i]]<-1 # add significance indicator
}
} # determine whether credible intervals span zero, and store as binary (to show in plot):
rm(temp)
mut_mod$sig<-mut_mod$sig %>% as.character() %>% parse_factor(levels = c('0','1')) # convert to factor
Plot_90 <- mut_mod %>%
ggplot(aes(y = Site_ID %>% as.character %>% parse_factor(levels = Site_order),
x = site_mean,
fill = stat(x) > 0,
shape = sig)) +
stat_halfeye(show.legend = F,slab_type = "pdf") +
geom_vline(xintercept = 0,
linetype = "dashed") +
scale_fill_manual(values = c("gray80","navajowhite")) +
scale_shape_manual(values = c(21,8)) +
labs(x = "Posterior draws (species effect)",
y = "") +
cowplot::theme_minimal_grid()Compare plots:
# plots #
plot_grid(Plot_50, Plot_75, Plot_90,
ncol = 3, align = "hv",
labels = c("Threshold: 0.5", "Threshold: 0.75", "Threshold: 0.9"))